{"Affiliation":[{"label":"Affiliation","value":"Applied Science, Faculty of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Civil Engineering, Department of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."}],"AggregatedSourceRepository":[{"label":"AggregatedSourceRepository","value":"DSpace","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","classmap":"ore:Aggregation","property":"edm:dataProvider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","explain":"A Europeana Data Model Property; The name or identifier of the organization who contributes data indirectly to an aggregation service (e.g. Europeana)"}],"Campus":[{"label":"Campus","value":"UBCV","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","classmap":"oc:ThesisDescription","property":"oc:degreeCampus"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","explain":"UBC Open Collections Metadata Components; Local Field; Identifies the name of the campus from which the graduate completed their degree."}],"Creator":[{"label":"Creator","value":"Loukas, Athanasios","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."}],"DateAvailable":[{"label":"DateAvailable","value":"2009-04-14T18:43:26Z","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"edm:WebResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"DateIssued":[{"label":"DateIssued","value":"1994","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"oc:SourceResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"Degree":[{"label":"Degree","value":"Doctor of Philosophy - PhD","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","classmap":"vivo:ThesisDegree","property":"vivo:relatedDegree"},"iri":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","explain":"VIVO-ISF Ontology V1.6 Property; The thesis degree; Extended Property specified by UBC, as per https:\/\/wiki.duraspace.org\/display\/VIVO\/Ontology+Editor%27s+Guide"}],"DegreeGrantor":[{"label":"DegreeGrantor","value":"University of British Columbia","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","classmap":"oc:ThesisDescription","property":"oc:degreeGrantor"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the institution where thesis was granted."}],"Description":[{"label":"Description","value":"A study of the precipitation distribution in coastal British Columbia is described and a technique is proposed for the reliable estimation of the frequency of rainfall generated floods from ungauged watersheds in the region. A multi-disciplinary investigation was undertaken encompassing the areas of hydrometeorology, meteorological modelling and hydrological modelling. Study components included analysis of long- and short-term precipitation in two medium sized watersheds located in southwestern coastal British Columbia; development of a 24-hour design storm for coastal British Columbia; generalization of the results over the coastal region of British Columbia; examination of the precipitation distribution during flood producing storms; identification of the applicability of a meteorological model for the estimation of short-term precipitation; and development of a physically-based stochasticdeterministic procedure for the estimation of flood runoff from ungauged watersheds of the region.\r\nBased on an assessment of the atmospheric processes which affect climate, it was\r\nfound that the strong frontal storms which form over the North Pacific Ocean and travel\r\neastward generate the majority of the precipitation during the winter and fall months, whereas\r\nconvective rainshowers and weak frontal storms produce the dry summer period precipitation.\r\nExamination of the annual, seasonal, and monthly precipitation in the two study\r\nwatersheds, the Seymour River and the Capilano River watersheds, showed that the variation\r\nof annual and winter and fall precipitation with elevation follows a curvilinear pattern,\r\nincreasing up to middle position of the watersheds at an elevation of about 400 m and then\r\ndecreasing or leveffing off at the upper elevations. The summer precipitation is more\r\nuniformly distributed over the watersheds than the winter precipitation and accounts for about 25% of the total annual precipitation. The Bergeron two-cloud mechanism has been identified as the dominant rainfall producing mechanism during the winter and fall months.\r\nAnalysis of regional data and results of other regional studies indicate that the\r\ncurvilinear pattern found in this study is more general and is similar for the whole of coastal\r\nBritish Columbia and the coastal Pacific Northwest.\r\nStudy of the 175 storms in the Seymour River watershed showed that the individual\r\nstorm precipitation is distributed in a pattern similar to that of the annual precipitation and this\r\ndistribution pattern is not affected by the type of the event. Furthennore, the analysis showed\r\nthat the storm time distribution is not affected by the elevation, type of the storm, its duration,\r\nand its depth. Also, analysis of the data from three sparsely located stations of coastal British\r\nColumbia indicated that the time distribution of the storms does not change significantly over\r\nthe region.\r\nWith regard to the development of techniques for the better estimation of flood runoff,\r\na 24-hour design storm has been developed by using the data from the Seymour River\r\nwatershed. Analysis of its spatial distribution revealed that this 24-hour design storm is\r\ndistributed in a similar patter to that of the annual precipitation. Also, it was found that the\r\n24-hour extreme raiiifall of various return periods is a certain percentage of the mean annual\r\nprecipitation. Comparison with regional data and results of other regional studies showed that\r\nthe developed design storm can be transposed over the whole coastal region of British\r\nColumbia. A comparative study and rainfall-runoff simulation for a real watershed showed\r\nthat from the widely used synthetic hyetographs, only the Soil Conservation Service Type IA\r\nstorm or the 10% time probability distribution curve of this study can accurately generate the\r\nflood runoff from watersheds of the region.\r\nThe above results of the short-term precipitation distribution with elevation and in time\r\nwere tested for extreme storms. Five periods of historical large flood producing storms were analyzed and it was shown that the fmdiiigs of the short-term precipitation analyses are valid\r\nfor these extreme storms.\r\nThe BOUNDP meteorological model was used for the estimation of storm\r\nprecipitation in the mountainous area which covers the two study watersheds, but the results\r\nshowed that this particular model is not capable of simulating the precipitation observed in the\r\narea. As a result, the initial intention of coupling the model with a hydrological model for the\r\nestimation of the runoff was abandoned.\r\nThe above results of the analysis of precipitation in coastal British Columbia and the\r\nfindings of previous research on the watershed response of coastal mountainous watersheds\r\nhave been combined and used for the development of a physically-based stochasticdeterministic\r\nprocedure. The procedure uses the method of derived distributions and Monte\r\nCarlo simulation to estimate the flood frequency for ungauged watersheds of the region. The\r\nprocedure has been tested with data from eight coastal British Columbia watersheds and\r\ncompared with the results of other widely used regional techniques. This comparison showed\r\nthat the method is reliable and efficient, and requires very limited data, which can be found from a topographical map and the Rainfall Frequency Atlas for Canada.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/description","classmap":"dpla:SourceResource","property":"dcterms:description"},"iri":"http:\/\/purl.org\/dc\/terms\/description","explain":"A Dublin Core Terms Property; An account of the resource.; Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource."}],"DigitalResourceOriginalRecord":[{"label":"DigitalResourceOriginalRecord","value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/7036?expand=metadata","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","classmap":"ore:Aggregation","property":"edm:aggregatedCHO"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","explain":"A Europeana Data Model Property; The identifier of the source object, e.g. the Mona Lisa itself. This could be a full linked open date URI or an internal identifier"}],"Extent":[{"label":"Extent","value":"7501462 bytes","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/extent","classmap":"dpla:SourceResource","property":"dcterms:extent"},"iri":"http:\/\/purl.org\/dc\/terms\/extent","explain":"A Dublin Core Terms Property; The size or duration of the resource."}],"FileFormat":[{"label":"FileFormat","value":"application\/pdf","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/elements\/1.1\/format","classmap":"edm:WebResource","property":"dc:format"},"iri":"http:\/\/purl.org\/dc\/elements\/1.1\/format","explain":"A Dublin Core Elements Property; The file format, physical medium, or dimensions of the resource.; Examples of dimensions include size and duration. Recommended best practice is to use a controlled vocabulary such as the list of Internet Media Types [MIME]."}],"FullText":[{"label":"FullText","value":"MOUNTAIN PRECIPITATION ANALYSIS FOR THE ESTIMATION OF FLOODRUNOFF IN COASTAL BRITISH COLUMBIAByATHANASIOS LOUKASDipL Eng., Aristotle University of Thessaloniki, 1988M.A.Sc., University of British Columbia, 1991A THESIS SUBMIYED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE STUDIESDEPARTMENT OF CIVIL ENGINEERINGWe accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAAugust 1994\u00a9 ATHANASIOS LOUKAS, 1994In presenting this thesis in partial fulfillment of therequirements for an advanced degree at the University of BritishColumbia, I agree that the Library shall make it freely availablefor reference and study. I further agree that permission forextensive copying of this thesis for scholarly purposes may begranted by the head of my department or by his or herrepresentatives. It is understood that copying or publication ofthis thesis for financial gain shall not be allowed without mywritten permission.(SiDepartment ofThe University of British ColumbiaVancouver, CanadaDate - \u2014\u2018-I5\u2019-IABSTRACTA study of the precipitation distribution in coastal British Columbia is described and atechnique is proposed for the reliable estimation of the frequency of rainfall generated floodsfrom ungauged watersheds in the region. A multi-disciplinary investigation was undertakenencompassing the areas of hydrometeorology, meteorological modelling and hydrologicalmodelling. Study components included analysis of long- and short-term precipitation in twomedium sized watersheds located in southwestern coastal British Columbia; development of a24-hour design storm for coastal British Columbia; generalization of the results over thecoastal region of British Columbia; examination of the precipitation distribution during floodproducing storms; identification of the applicability of a meteorological model for theestimation of short-term precipitation; and development of a physically-based stochastic-deterministic procedure for the estimation of flood runoff from ungauged watersheds of theregion.Based on an assessment of the atmospheric processes which affect climate, it wasfound that the strong frontal storms which form over the North Pacific Ocean and traveleastward generate the majority of the precipitation during the winter and fall months, whereasconvective rainshowers and weak frontal storms produce the dry summer period precipitation.Examination of the annual, seasonal, and monthly precipitation in the two studywatersheds, the Seymour River and the Capilano River watersheds, showed that the variationof annual and winter and fall precipitation with elevation follows a curvilinear pattern,increasing up to middle position of the watersheds at an elevation of about 400 m and thendecreasing or leveffing off at the upper elevations. The summer precipitation is moreuniformly distributed over the watersheds than the winter precipitation and accounts for about1125% of the total annual precipitation. The Bergeron two-cloud mechanism has been identifiedas the dominant rainfall producing mechanism during the winter and fall months.Analysis of regional data and results of other regional studies indicate that thecurvilinear pattern found in this study is more general and is similar for the whole of coastalBritish Columbia and the coastal Pacific Northwest.Study of the 175 storms in the Seymour River watershed showed that the individualstorm precipitation is distributed in a pattern similar to that of the annual precipitation and thisdistribution pattern is not affected by the type of the event. Furthennore, the analysis showedthat the storm time distribution is not affected by the elevation, type of the storm, its duration,and its depth. Also, analysis of the data from three sparsely located stations of coastal BritishColumbia indicated that the time distribution of the storms does not change significantly overthe region.With regard to the development of techniques for the better estimation of flood runoff,a 24-hour design storm has been developed by using the data from the Seymour Riverwatershed. Analysis of its spatial distribution revealed that this 24-hour design storm isdistributed in a similar patter to that of the annual precipitation. Also, it was found that the24-hour extreme raiiifall of various return periods is a certain percentage of the mean annualprecipitation. Comparison with regional data and results of other regional studies showed thatthe developed design storm can be transposed over the whole coastal region of BritishColumbia. A comparative study and rainfall-runoff simulation for a real watershed showedthat from the widely used synthetic hyetographs, only the Soil Conservation Service Type IAstorm or the 10% time probability distribution curve of this study can accurately generate theflood runoff from watersheds of the region.The above results of the short-term precipitation distribution with elevation and in timewere tested for extreme storms. Five periods of historical large flood producing storms were111analyzed and it was shown that the fmdiiigs of the short-term precipitation analyses are validfor these extreme storms.The BOUNDP meteorological model was used for the estimation of stormprecipitation in the mountainous area which covers the two study watersheds, but the resultsshowed that this particular model is not capable of simulating the precipitation observed in thearea. As a result, the initial intention of coupling the model with a hydrological model for theestimation of the runoff was abandoned.The above results of the analysis of precipitation in coastal British Columbia and thefindings of previous research on the watershed response of coastal mountainous watershedshave been combined and used for the development of a physically-based stochastic-deterministic procedure. The procedure uses the method of derived distributions and MonteCarlo simulation to estimate the flood frequency for ungauged watersheds of the region. Theprocedure has been tested with data from eight coastal British Columbia watersheds andcompared with the results of other widely used regional techniques. This comparison showedthat the method is reliable and efficient, and requires very limited data, which can be foundfrom a topographical map and the Rainfall Frequency Atlas for Canada.ivTABLE OF CONTENTSABSTRACT iiLIST OF TABLES xiLIST OF FIGURES xiiiACKNOWLEDGMENT xxii1. INTRODUCTION 12. STUDY AREA AND DATA SETS 62.1 Regional Climate 62.2 The Study Watersheds 102.2.1 Topography 102.2.2 Interaction of weather systems with the local topography 122.3 Data Sets 133. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTION 243.1 Introduction 243.2 Spatial Distribution of Precipitation 253.2.1 Annual and seasonal precipitation distribution in the SeymourRiver valley 253.2.2 Annual and seasonal precipitation distribution in the CapilanoRiver valley 273.2.3 Monthly precipitation distribution in the two study watershedvalleys 29v3.2.4 Comparison of mountain and valley precipitation 323.3 Temporal Variation of Precipitation 353.3.1 Seymour river watershed 353.3.2 Capilano river watershed 363.4 Spatial Variation of Precipitation 363.4.1 Seymour river watershed 373.4.2 Capilano river watershed 393.5 Comparison with Other Studies and Regional Data 403.6 Meteorological Mechanisms Affecting the Precipitation Distribution 453.7 Summary 474. STORM PRECIPITATION DISTRIBUTION 634.1 Introduction 634.2 Data Sets 644.3 Spatial Distribution of Storms 664.3.1 Storm precipitation 664.3.1.1 Spatial variation 674.3.2 Duration and average storm intensity 694.3.3 Maximum hourly intensity 704.3.4 Relative start time 704.4 Time Distribution of Storms 714.4.1 Research Procedure 724.4.2 Results 744.5 Antecedent Precipitation 784.6 Summary 80vi5. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA 985.1 Introduction 985.2 Data Sets and Method of Analysis 1005.3 Time Distribution 1025.4 Spatial Distribution 1065.5 Antecedent Rainfall 1115.6 Simulation of Peak Storm Flow at Jamieson Creek Watershed 1135.7 Summary 1176. STUDY OF ifiSTORICAL LARGE STORMS 1336.1 Introduction 1336.2 The July 11-12, 1972 Rainstorm 1346.2.1 Synoptic conditions 1346.2.2 Spatial distribution 1356.2.3 Time distribution 1366.3 The December 13-18, 1979 Rainstorms 1376.3.1 Synoptic conditions 1376.3.2 The December 13-14, 1979 storm 1386.3.2.1 Spatial distribution 1386.3.2.2 Time distribution 1406.3.3 The December 16-18, 1979 storm 1406.3.3.1 Spatial distribution 1406.3.3.2 Time distribution 1416.4 The October 25-31, 1981 Rainstorms 1426.4.1 Synoptic conditions 142vii6.4.2 The October 25-28, 1981sto. 1436.4.2.1 Spatial distribution 1436.4.2.2 Time distribution 1446.4.3 The October 28-3 1, 1981 storm 1456.4.3.1 Spatial distribution 1456.4.3.2 Time distribution 1466.5 The November 8-11, 1990 Rainstorm 1466.5.1 Synoptic conditions 1466.5.2 Spatial distribution 1476.5.3 Time distribution 1496.6 The November 21-24, 1990 Rainstorm 1496.6.1 Synoptic conditions 1496.6.2 Spatial distribution 1496.6.3 Time distribution 1516.7 Summary 1517. APPLICATION 01? A METEOROLOGICAL MODEL 1607.1 Introduction 1607.2 General Description of the BOUNDP Model 1637.2.1 Overview 1637.2.1.1 The wind model 1647.2.1.2 The water flux model 1667.2.1.3 Estimation of precipitation 1687.3 Data Sets 1697.4 Application 171viii7.4.1 Comp1icaons . 1717.4.2 Results 1737.4.2.1 Calibration of the model 1747.4.2.2 Analysis of the regression coefficients 1777.4.2.3 Verification of the model 1797.5 Summary 1818. A PHYSICALLY BASED STOCHASTIC-DETERMINISTIC PROCEDUREFOR THE ESTIMATION OF FLOOD RUNOFF 2028.1 Introduction 2028.2 Procedure 2078.2.1 Rainfall model 2088.2.2 Watershed response model 2128.3 Application and Results 2168.4 Sensitivity Analysis 2208.5 Comparison with Regional Techniques 2258.5.1 Methods 2268.5.1.1 Index flood method 2268.5.1.2 Method of direct regression of quantiles (DRO) 2278.5.1.3 Method of regression for distribution parameters (RDP) 2278.5.1.4 B.C. Environment method 2288.5.1.5 Russell\u2019s Bayesian methodology 2298.5.2 Application 2308.5.3 Results 2388.6 Summary 242ix9. CONCLUSIONS AND RECOMMENDATIONS .2729.1 Conclusions 2729.2 Recommendations 276REFERENCES 279APPENDICES 299A PRECIPITATION AND STREAMFLOW STATIONS USED IN THE STUDY OFLONG-TERM PRECIPITATION 299B RELATIONSHIP BETWEEN EXTREME 24-HOUR RAINFALL AND MEANANNUAL PRECIPITATION 309C CHARACTERISTICS OF THE 44 BASINS USED FOR THE TESTING OF THEMODIFIED SNYDER FORMULA 318xLIST OF TABLES2.1 Mean monthly precipitation for representative coastal British Columbia stations... 82.2 Precipitation stations in the Seymour River watershed 142.3 Precipitation stations in the Capilano River watershed 153.1 Regression coefficients for the monthly precipitation-Seymour River watershed... 303.2 Regression coefficients for the monthly precipitation-Capilano River watershed... 313.3 Comparison of the annual precipitation gradient for the valley and mountain slope333.4 Regression coefficients for the monthly precipitation-Lower Capilano valley,Hollyburn and Grouse mountains 344.1 Characteristics of the coastal British Columbia stations whose data analyzed 775.1 Characteristics of the coastal British Columbia stations used in the analysis of the24-hour extreme rainfall time distribution 1045.2 Ratio of the 24-hour rainfall and mean annual precipitation for various coastalsubregions of British Columbia 1105.3 Probability distribution of antecedent rainfall for the maximum 24-hour storms forvarious number of days 1125.4 Comparison of the simulated peak flows (m3\/sec) using various hyetographs withthe observed flows of Jamieson Creek watershed 1167.1 Precipitation stations used in the application of the BOUNDP model 1758.1 The characteristics of the eight watersheds used in the study 2198.2 Sensitivity Index Values (SI, %) for the mean annual 24-hour rainfall (Rm) 2228.3 Sensitivity Index Values (SI, %) for the standard deviation of the 24-hourrainfall (CYR) 2228.4 Sensitivity Index Values (SI, %) for the mean of storage factor(11m) 223xi8.5 Sensitivity Index Values (SI, %) for the coefficient of variation of KF (CV1)... 2238.6 Sensitivity Index Values (SI, %) for the mean of infiltration abstractions parameter(If) 2248.7 Sensitivity Index Values (SI, %) for the coefficient of variation of I (CVJf) 2248.8 Sensitivity Index Values (SI, %) for the form of the parameters 2258.9 Characteristics of coastal British Columbia watersheds used in the study 2328.10 Regional equations of instantaneous peak flow for the method of Direct Regressionof Quantiles 2348.11 Regional equations of daily peak flow for the method of Direct Regression ofQuantiles 2358.12 Comparison of estimated instantaneous peak flow (m3\/sec) for various returnperiods using various methods 2408.13 Comparison of estimated daily peak flow (m3\/sec) for various return periodsusing various methods with the fitted Extreme Value type I distribution and theobserved flows 241Al Precipitation stations in coastal British Columbia 300A2 Streamfiow gauging stations in coastal British Columbia 307Bi Characteristics of the sixty-one stations used in the analysis of the 24-hour extremerainfall 310Cl Characteristics of the basins used in the independent test of the modified Snyderformula 319xliLIST OF FIGURES2.1 Map showing coastal British Columbia 192.2 Mean monthly temperatures for coastal British Columbia stations 202.3 The location and instrumentation of the study watersheds 212.4 Area-elevation curves for the two study watersheds 222.5 Comparison of the precipitation accumulations with the snow course data 233.1 The distribution of the annual and seasonal precipitation along the topographicprofile of the Seymour River watershed 493.2 The distribution of the annual and seasonal precipitation along the topographicprofile of the Capilano River watershed 503.3 The coefficients of variation of the annual and seasonal precipitation at theSeymour River watershed 513.4 (a) The distribution of the monthly precipitation at selected stations at the SeymourRiver watershed and (b) its coefficients of variation 523.5 The coefficients of variation of the annual and seasonal precipitation at theCapilano River watershed 533.6 (a) The distribution of the monthly precipitation at selected stations at the CapilanoRiver watershed and (b) its coefficients of variation 543.7 Monthly distribution of the correlation coefficient between several stationsin the Seymour River watershed 553.8 Spatial correlation functions of annual, seasonal, and November and Augustprecipitation in the Seymour River watershed 56xlii3.9 Monthly distribution of the correlation coefficient between several stations in theCapilano River watershed 573.10 Spatial correlation functions of annual, seasonal, and November and Augustprecipitation in the Capilano River watershed 583.11 (a) Distribution of the annual and seasonal precipitation with elevation and (b)coefficients of variation for the annual and seasonal precipitation at differentelevations in the North Cascades, Washington State (after data of Rusmussen andTangborn, 1976) 593.12 Distribution of the mean annual runoff with mean basin elevation for northernCascades region (after data of Rasmussen and Tangborn, 1976) 603.13 Distribution of annual precipitation (a) and its coefficients of variation (b) withelevation for the coastal British Columbia stations (269 stations) 613.14 Distribution of the mean annual runoff with mean basin elevation for coastalBritish Columbia stations 624.1 a) Monthly distribution of the average annual precipitation at station S-i andb) Monthly distribution of the 175 storms analyzed 824.2 a) Precipitation ratio to base station (Vancouver Harbour) for various stationsand types of events and b) its coefficient of variation 834.3 Spatial correlation functions for the various types of storms 844.4 (a) Storm continuity at various elevations and types of storms and (b) Coefficientof variation of storm continuity 854.5 (a) Storm duration ratio to base station for various elevations and types of stormsand (b) Coefficient of variation of storm duration ratio 86xiv4.6 (a) Ratio of the average storm intensity to base station for various elevations and typesof storms and (b) its coefficient of variation 874.7 Ratio of the maximum hourly intensity to base station for various elevations andtypes of storms and (b) its coefficients of variation 884.8 Storm relative start time to the base station at different elevations and types ofstorms 894.9 Time distribution probability curves at station 25B 904.10 Comparison of the time distribution probability curves for different stations andelevations in the Seymour River watershed 914.11 Comparison of the time distribution probability curves for different type of eventsat the station 25B in the Seymour River watershed 924.12 The effect of the storm duration on the time probability distribution curves(Station S-i) 934.13 The effect of the storm precipitation on the time probability distribution curves(Station S-i) 944.14 Comparison of the time probability distribution curves for the Seymour Riverwatershed and three coastal British Columbia stations 954.15 Probability of equality or exceedance of antecedent precipitation at station S-i for(a) October to March storms and (b) April to September storms 964.16 Comparison of the probability of equality or exceedance of the antecedentprecipitation for different elevations for (a) winter and (b) summer 975.1 Distribution of the coastal British Columbia stations with (a) years of record and(b) station elevation 1205.2 Monthly distribution of the occurrence of the annual maximum 24-hour storms atVancouver Harbour (53 years) 121xv5.3 Time probability distributions of 24-hour storms for station S-i 1225.4 Comparison of the time probability distributions for various storms a) ten percentcurves, b) fifty percent curves, and c) ninety percent curves 1235.5 Average time probability distributions for the Seymour River watershed 1245.6 Comparison of the average storm time distribution in the Seymour River watershedwith the results of the Melone (1986) analysis for coastal British Columbia 1265.7 Comparison of the average storm time distribution in the Seymour River watershedwith the results of the Hogg (1980) analysis for coastal British Columbia 1275.8 Comparison of the time probability distributions of the Seymour River watershedwith three coastal British Columbia stations, a) ten percent curves, b) fifty percentcurves, and c) ninety percent curves 1285.9 Comparison of synthetic storms with the average time probability distributions ofthe Seymour River watershed 1295.10 Distribution of rainfall with elevation for various return periods in the SeymourRiver watershed 1305.11 Relationship of the 10-year 24-hour rainfall and mean annual precipitation for thesixty-one recording stations in the coastal British Columbia 1315.12 The watershed model flow chart 1325.13 Estimation of the 10-year flood for the Jamieson Creek watershed using syntheticand derived hyetographs 1336.1 Comparison of the time distribution of the July 11-12, 1972 storm with timeprobability distribution curves at (a) station 1OA and (b) station 14A 1536.2 Comparison of the time distribution of the December 12-14, 1979 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station 1OA and(c) station 14A 154xvi6.3 Comparison of the time distribution of the December 16-19, 1979 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station 10A and(c) station 14A 1556.4 Comparison of the time distribution of the October 25-28, 1981 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station 1OA,(c) station 14A and (d) station 25B 1566.5 Comparison of the time distribution of the October 28-3 1, 1981 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station bA,(c) station 14A and (d) station 25B 1576.6 Comparison of the time distribution of the November 8-11, 1990 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station 14A and(c) station 25B 1586.7 Comparison of the time distribution of the November 2 1-24, 1990 storm with timeprobability distribution curves at (a) station Vancouver Harbour, (b) station 1OA and(c) station 14A 1597.1 Three dimensional map of the calculation domain (latitude and longitude in degreesand elevation in meters with vertical scale 1: 17,500) 1827.2 Three dimensional map of the model domain (latitude and longitude in degrees andelevation in meters with vertical scale 1:32,500) 1837.3 Topographical contour map of the calculation domain (latitude and longitude indegrees) 1847.4 Topographical contour map of the model domain (latitude and longitude indegrees) 1857.5 Undisplaced water flux (mm) for November 10, 1990 (12:00 UTC) 1867.6 Undisplaced water flux (mm) for November 11, 1990 (00:00 UTC) 187xvii7.7 Displaced water flux (mm) for November 10, 1990 (12:00 UTC) 1887.8 Displaced water flux (mm) for November 11, 1990 (00:00 UTC) 1897.9 Predicted precipitation (mm) for November 10, 1990 1907.10 Objectively analyzed precipitation (mm) for November 10, 1990 1917.11 Scattergraphs of observed and predicted precipitation for calibration for a) November10, 1990 and b) total storm period between November 8-13, 1990 1927.12 Regression coefficients versus the average domain precipitation a) Al and b) A2 1937.13 Regression coefficients versus the average domain precipitation a) A3 and A4 1947.14 Regression between the average domain precipitation and the coefficient Al 1957.15 Regression between the precipitation difference between U.B.C. and Grousemountain resort and the coefficient A4 1967.16 Undisplaced water flux (mm) for August 29, 1991(12:00 UTC) 1977.17 Undisplaced water flux (mm) for August 30, 1991 (00:00 UTC) 1987.18 Displaced water flux (mm) for August 29, 1991 (12:00 UTC) 1997.19 Displaced water flux (mm) for August 30, 1991 (00:00 UTC) 2007.20 Scattergraphs of observed and predicted precipitation for verification for a) August28, 1991 and b) total storm between August 26-30, 1991 2018.1 Isopleths of the mean annual 24-hour rainfall in coastal British Columbia(After Rainfall Frequency Atlas for Canada, Hogg and Carr, 1985) 2448.2 Isopleths of the standard deviation of the mean annual 24-hour rainfall in coastalBritish Columbia (After Rainfall Frequency Atlas for Canada,Hogg and Carr, 1985) 2458.3 Comparison of the frequency of the observed and simulated hourly peak flowusing the 24-hour, the 12-hour and the 6-hour storms for the CarnationCreek watershed 246xviii8.4 Comparison of the observed and simulated cumulative time probability distributionsfor the coastal British Columbia 2478.5 Scattergraph between computed and observed time lag for 43 North America basins(Data after Watt and Chow, 1985) 2488.6 Flow chart of the Monte Carlo simulation 2498.7 Map showing the location of the eight coastal British Columbia watersheds wherethe proposed procedure has been applied 2508.8 Flood frequency curves for the Capilano River watershed a) hourly flows,b) daily flows and c) flood volume 2518.9 Flood frequency curves for the Carnation Creek watershed a) hourly flows,b) daily flows and c) flood volume 2528.10 Flood frequency curves for the Chapman Creek watershed a) hourly flows,b) daily flows and c) flood volume 2538.11 Flood frequency curves for the Zeballos River watershed a) hourly flows,b) daily flows and c) flood volume 2548.12 Flood frequency curves for the North Allouette River watershed a) hourly flows,b) daily flows and c) flood volume 2558.13 Flood frequency curves for the Oyster River watershed a) hourly flows, b) daily flowsand c) flood volume 2568.14 Flood frequency curves for the Hirsch Creek watershed a) hourly flows, b) daily flowsand c) flood volume 2578.15 Flood frequency curves for the San Juan River watershed a) hourly flows,b) daily flows and c) flood volume 2588.16 Sensitivity of the procedure to the change of mean 24-hour rainfall depth (Rm) fora) hourly flow, b) daily flow and c) flood volume for CarnationxixCreek watershed .2598.17 Sensitivity of the procedure to the change of standard deviation of the 24-hourannual rainfall (a\u2019R) for a) hourly flow, b) daily flow and c) flood volumefor Carnation Creek watershed 2608.18 Sensitivity of the procedure to the change of mean storage factor of fast runoff(KFm) for a) hourly flow, b) daily flow and c) flood volume for CarnationCreek watershed 2618.19 Sensitivity of the procedure to the change of coefficient of variation of storagefactor (CV1
.\/u 21 \/O\/-. \u2018ji20- \/19 \u2014.,o18- \u2014..LU \u2014\u2014O 17- \\..o \u2018S16II5- \u2014\u2014\u2014\u2014I14o\u2018 4 \u2018 8 \u2018 12 \u2018 16 \u2018 20 \u2018 24 28 32DISTANCE (km)Fig. 3.3. The coefficients of variation of the annual and seasonal precipitationat the Seymour River watershed.51z00Izw()UULii0C)200 -100090Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTION- - -- Vancouver Harbour\u2014\u2014 s-i14A28A700600500z400 -300 -\u2014\u2022(a)\/ .%.\/ N.\/\/\/\/I\/\/IIII I I I I I IJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC- - -- Vancouver Harbour\u2014\u2014 s-i14A28A8070605040\/(b)30 -20 I I I I I IJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMONTHSFig. 3.4. (a) The distribution of the monthly precipitation at selected stationsat the Seymour River watershed and (b) Its coefficients of variation.52Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRiBUTION28 \u201427 - - -- ANNUALS... S..\u2014\u2014 OCT-MAR25Z 24 APR-SEP023\/22 \u201c,. \/S... \u2022 \/21 \u2018S. \/S...2O \/- S \/ \/19 .18 S..\u2019 \/LI.. \/b17 S..\u2019.\\ \/0o16 \u201c15 - \u201c I 714 - \u2014 \u2014 _,\u2014__II13 I I I0 10 20 30 40DISTANCE (km)Fig. 3.5. The coefficients of variation of the annual and seasonal precipitationat the Capilano River watershed.53Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTION700600500z400300\u00b0 2001000 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC100JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMONTHSFig. 3.6. (a) The distribution of the monthly precipitation at selected stationsat the Capilano River watershed and (b) its coefficients of variation.- - -- Vancouver Harbour (a)\u2014\u2014 Cleveland DamC-4C-5\u2014\u2014\u2014%..,\/\/\/\/\/\/\/ ,.\u2014I I I I I I I I I I- - -- Vancouver Harbour\u2014\u2014 Cleveland DamC-40-5z0I9080706050403020(b)54Chapter 3. ANNUAL AND SEASONAL PRECIPITATIONDISTRIBUTIONI\u2014zwC)LIUw00zI\u2014.\u2014\u2014 s**\u2014 -. \u2014 \u2014 \u2014._c\u2014\u2014-\u2014 \u2014 -\/\u2018-V\u201910.90.80.70.60.50.40.30.20.1010.90.80.70.60.50.40.30.20.10I0.90.80.70.60.50.40.30.20.10- - -- S-I - Seymour Falls Dam\u2014\u2014 S-I-bAS-I-28AJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC\u2014 -\u2014-- N_._._\u2014-\u20221\/\/\/ - - - - 28A-I 4A\\ \/\u2018 \u2014 \u2014 28A-S228A - Seymour Falls DamJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMONTHSFig. 3.7. Monthly distribution of the correlation coefficient betweenseveral stations in the Seymour River watershed.551\u201409 0.8--\u2014:-----..\u2014:-----..\u2014.-\u2014\u2014\u2014\u2014-\u2014.\u2014\u2022\u2014\u202207\u2014.\u2014.\u2014\u2022I-.-\u2014\u2014z\u2014\u2014Lu\u2014\u2014--\u2022=-c0.6it Lu o o0.5ANNUAL0.4SEASONALOCT-MAR0.3\u2014\u2014-SEASONALAPR-SEPoNOVEMBER0.2-AUGUST0.1-0\u2014IIIIIIIIII048121620242832DISTANCE(km)Fig.3.8.Spatialcorrelationfunctionsof annual,seasonal,andNovemberandAugustprecipitationintheSeymourRiverwatershed.10.90.80.70.60.50.40.30.20.10I0.90.80.70.60.50.40.30.20.1010.90.80.70.60.50.40.30.20.10Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTION- - -- Vancouver Harbour - 0-2\u2014\u2014 Vancouver Harbour - C-3Vancouver Harbour - 0-5JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECIzw0UUw0C)z0IIIS_S\u2018V\u2019L..\u2014. St S\u20145% 5 F\u201455 SF\u2018,- - -- 0-2 - Capilario\u2014\u2014 0-2-0-3C-2-C-5JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC.-.\u2014.\u2014.--.\u2014 \u2014_-.S.\u2014 \u2014 \u2014S_F %51055% I,- - -- 0-5 - Cleveland Dam\u2014\u2014C-5-C-40-50-3JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMONTHSFig. 3.9. Monthly distribution of the correlation coefficient betweenseveral stations in the Capilano River watershed.57.1______-..\u2014--.\u2014.\u2014..-:::: 0.7- 0.6-Ui0o o0.5-0.4-ANNUALw-SEASONALOCT-MAR0.3\u2014\u2014-SEASONALAPR-SEP0-..-..-NOVEMBER0.2-AUGUST0.1-0IIIIIIIIIIIII048121620242832DISTANCE(km)Fig.3.10.Spatialcorrelationfunctionsofannual,seasonal,andNovemberandAugustprecipitationintheCapilanoRiverwatershed.Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTIONz0U0IzUi0UUUi00STATION ELEVATION (m)Fig.3.1 1 a) Distribution of the annual and seasonal precipitation with elevationand (b) coefficients of variation for the annual and seasonal precipitationat different elevations in the North Cascades, Washington State.(after data of Rasmussen and Tangbom, 1976)z00.0Ui040003500ANNUAL (a)t WINTER30000 SUMMER+2500a a \u2022+\u2022 a + a2000 .1+ a+1500 a + + +1000 a50000 e0 II+0 20040060050403020100800 1000 1200 1400 1600 180000 0a+0000ANNUALWINTERSUMMER(b)90000+a00a+aIa0 200 400 600 800 1000 1200 1400 1600 1800595000\u2014SouthForkCascadeRiver4000-U\u20143000EUL1\u2022.LL 0UUzU2000-1000-U0IIIIIII0200400600800100012001400160018002000MEANBASINELEVATION(m)Fig.3.12.Distributionofthemeanannual runoffwithmeanbasinelevationfor northernCascadesregion(afterdataofRasmussenandTangbom, 1976)Chapter 3. ANNUAL AND SEASONAL PRECIPITATION DISTRIBUTION500045004000350030002500200015001000500032302826Z 2402220> 180Uw 140o 121086STATION ELEVATION (m)Fig. 3.13. Distribution of annual precipitation (a) and its coefficient of variation (b)with elevation for coastal British Columbia stations (269 stations).0 200 400 600 800 1000 12000 200 400 600 800 1000 1200615000\u2014\u2022ZeballosRiver450040003500\u20223000E0LL u25001oIII2000I\u2022I.\u2022II1500\u2022II1000 500\u20220020040060080010001200MEANBASINELEVA11ON(m)Fig.3.14.DistributionofthemeanannualrunoffwithmeanbasinelevationforcoastalBritishColumbiastations.CHAPTER 4STORM PRECIPITATION DISTRIBUTION4.1 IntroductionThe precipitation distribution and variability in longer time-scales namely, annual, seasonaland monthly scales is adequate for long-term reservoir operation, water supply and irrigation.On the other hand, the knowledge of precipitation in short time-scales like daily, hourly andduring a storm is necessary for the simulation of runoff and especially for flood flows. Theimportance of adequately defining the spatial and temporal distribution of storm precipitationfor modeling streamfiow and evaluating the runoff response of a watershed has been wellrecognized among hydrologists (Beven and Hornberger, 1982; Bras et al, 1985; Watts andCalver, 1991). The need for better estimation, description and modeling of precipitation hasled hydrologists to identify the spatial and time variation of precipitation and quantify thisvariation in many different climates (Sharon, 1980; Bemdtsson and Niemcynowitz, 1986;Hughes and Wright, 1988; Corradini and Melone, 1989; Wheater et al, 1991). Theimportance of the precipitation distribution becomes critical for the mountainous watershedswhere the weather systems interact with the topography resulting in highly non-uniformprecipitation over the area. Hence, it is necessary to study the precipitation in detail, in orderto understand its distribution and, consequently, be able to predict the river flows withimproved accuracy.The objective of this chapter is to present the results of the study of spatial and timedistribution of storm precipitation in the Seymour River watershed, to compare the results63Chapter 4. STORM PRECIPITATION DISTRIBUTIONwith data from other coastal British Columbia stations and finally, to identify similaritiesbetween the distribution of the storm precipitation and the annual and seasonal precipitation.4.2 Data SetsThe data for the analysis have been taken from six recording stations in the Seymourwatershed for the period December 1983 to December 1990. The data from the stations S-i,1OA, 14A, 21A, 25B and 28A have been used (Table 2.2 and Fig. 2.3). The data set from theVancouver Harbour station is used to assess the zero elevation precipitation. The stations areunder the jurisdiction of two organizations, the Atmospheric Environment Service (A.E.S.)and the Faculty of Forestry of the University British Columbia (U.B.C.).Data in hourly time increments have been used. The stations 21A and 28A (Table2.2) are equipped with charts that can be read with an accuracy of three hours. The other fourstations the precipitation can be read in hourly increments. Therefore, the stations 21A and28A will not be used in the analysis of intensity and duration but they will be used in the studyof the storm precipitation distribution.A storm is defined as the precipitation period separated from the preceding andsucceeding rainfall by 6 or more hours at all stations. All storms used had mean watershedprecipitation exceeding 20 mm, and the average storm intensity was larger than 1 mm\/h.Within the data period 175 network storms having total duration from 10 hours to 7 daysqualified for the study.Attention is paid so that the seasonal distribution of the storms analyzed follows themonthly precipitation distribution. Figure 4.1 compares the monthly distribution of theaverage annual precipitation distribution at station S-l with the monthly distribution of the175 storms analyzed.64Chapter 4. STORM PRECIPITATION DISTRIBUTIONThe storms have been classified as rainfall events, mixed ram and snow events andsnowfall events according to whether the temperature at 762 m elevation (station 25B) wasabove 2\u00b0C, between 00 and 2\u00b0C or below 0\u00b0C, respectively. The transitional air temperaturefrom snow to rain has been examined by Rohrer (1989). Rohrer determined that thistemperature is around 2\u00b0C. This same transitional temperature is used in the U.B.C.watershed model (Quick, 1993) for the classification of the precipitation into snow and rain.The rainfall events have also been separated into summer and winter storms toexamine if the distribution is different for the winter frontal and the summer convective rains.Forty-three events (25% of the 175 events) were classified as rainfall storms of the October-March period, fifty-three events (30% of the 175 events) as April to September rain storms,forty-four (25% of the 175 events) as snowfalls, and thirty five events (20% of the 175events) as mixed snow and rain events.The division of the events on the above categories has been made using air temperaturedata from the Vancouver Harbour and the 25B stations. The majority of the events had aduration larger than 24 hours so that the mean daily temperature was assumed to give theaverage temperature during the storms. For the periods of missing temperature data at station25B, the Vancouver Harbour data and an average temperature lapse rate of 0.90C per 100 melevation was used to estimate the mean daily temperature at the higher elevation stations inthe Seymour River watershed. This lapse rate was found from the temperature data at thestations Vancouver Harbour and 25B. It should be noted that during a particular storm it maysnow at the upper elevations, while raining at the middle and lower elevations. Hence, thesame storm can produce three types of precipitation, rainfall, snowfall, and mixed rain andsnow, over the elevation range of the study watershed.65Chapter 4. STORM PRECIPITATION DISTRIBUTION4.3 Spatial Distribution of StormsThe spatial distribution of the storms in the Seymour River watershed is investigated byexamining the ratio of the value of certain storm features at each of the stations compared withthe base station, Vancouver Harbour. The storm features analyzed are: the storm depth, thestorm duration, the average storm intensity, the maximum hourly intensity and the relativestart time of the storm. The spatial distribution of each of these storm features will beexamined.4.3.1 Storm precipitationBased on the precipitation measured at the base station, the ratio of precipitation for all typesof events increases to about 3.5 at 260 m (station S-i), and then decreases abruptly to about2.8 at 293 m (station bA). This large decrease coincides with the turn of the Seymour Rivervalley to the northwest. For rainfall events during the winter period (October to March) theratio seems to stabilize at a value of about 2.7 at the upper watershed (Fig. 4.2a). The April toSeptember rain events show a leveling of the ratio up to an elevation of about 600 m and thenthe ratio increases to a maximum of 3.2 at 853 m (station 28A) (Fig. 4.2a). For the mixedevents there is a constant decrease of the ratio to about 2.55 at 853 m (Fig. 4.2a). Thesnowfall events show a decrease in the ratio to about 2.3 at 600 m elevation, and then the ratioincreases to 2.8 at 853 m (Fig. 4.2a).The variation of the precipitation ratio increases up to the station S-i (293 m), andthen either levels off or increases. For the winter rainfall events the variation decreases afterstation S-i, but then levels off before increasing at the upper station. For the summer rainfallsthe variation increases slowly for all elevations. The snow events and the mixed events show66Chapter 4. STORM PRECIPITATION DISTRIBUTIONsimilar variation, except for the snow events in which the variation increases at the top stationat 853 m. Most of the variation at the upper watershed is high, between 30-60 % (Fig. 4.2b).The above results indicate that the precipitation always increases with elevation up tothe middle watershed, and then decreases and levels off at the upper elevations. This isevident for the winter rainfall storms and the mixed rain-snow events. However, theprecipitation during the snow events and the summer rainfalls increases, on average, at the topstation 28A at 853 m. A possible explanation is that at low temperatures the air is becomingmore stable and after the subsidence that occurs immediately after the middle watershed, thesnowfall increases again as the air is forced to lift over the steep headwater slopes of thewatershed. Somewhat unexpectedly, there is a similar increase for summer rain events in theupper watershed, when convective instability may occur as the warm air is forced to rise overthe steep slopes, generating larger precipitation and larger intensities at the upper watershedduring the summer months (Barry, 1992). Data from more stations are required tosubstantiate this precipitation increase at the upper elevations during the snow and summerrain events, so that no definite conclusions can be made at present.The above results also show that the storm precipitation has a similar distributionpattern to the distribution of the long-term precipitation, although the increase of stormprecipitation with elevation is larger than the annual and seasonal precipitation increase. Thestorms analyzed in this study are the larger storms, which must therefore have a steeperprecipitation gradient than the smaller storms.4.3.1.1 Spatial variationTo study the spatial variability of the storm precipitation the correlation coefficient (r) is usedas it has been used in Chapter 3 for the annual precipitation. It is again assumed that the67Chapter 4. STORM PRECIPITATION DISTRiBUTIONcorrelation coefficient is only function of distance. The underlying assumption of the spatialcorrelation is that the precipitation field is homogeneous and isotropic.The spatial correlation functions developed in this way using the data sets for the 175storms from the seven stations are:Rainfall October-March r(d) = 0.942exp(-0.005d) (4.1)Rainfall April-September r(d) = 0.924exp(-0.008d) (4.2)Snow r(d) = 0.925exp(-0.003d) (4.3)Mixed Rain and Snow r(d) = 0,999exp(-0.OlOd) (4.4)Figure 4.3 shows the above correlation functions. All the types of storms have correlationcoefficients larger than 0.75 for distances smaller than 32 km. Furthermore, the snow and thewinter rainfall events have similar correlation functions and their values are always larger thanthe correlations for the April-September events.These results show that during the snow and winter rainfall events the precipitation isthe least variable in space and more consistent relationships exist between the stormaccumulations of the various stations. The precipitation during the winter months is generatedby strong frontal systems, and these systems cover large areas, producing more uniformprecipitation over the medium-sized watershed.On the other hand, the largest spatial variability is observed during the summer storms.The precipitation during this period of the year is produced by weak frontal systems andconvective rains. Even though these types of precipitation systems cover the wholewatershed, they produce more variable precipitation than the winter storms, but even so theoverall spatial variability of precipitation can still be considered small.68Chapter 4. STORM PRECIPITATION DISTRiBUTION4.3.2 Duration and average storm intensityThe total duration of the storm is assessed as the time when precipitation is first recorded atany station and the time that it ceases at all stations. At each individual station, the duration isthe time period in which non-zero precipitation was recorded.The ratio of storm duration at each station divided by the total duration gives anindication of the continuity of precipitation. The average duration of storm at zero elevation(Vancouver Harbour) is only 45 % of the total duration, whereas it is about 75 % at the midand upper watershed. No significant differences have been observed for the different type ofevents (Fig. 4.4a). The variation of the storm continuity is larger (30 %-50 %) at the zeroelevation and smaller (20 %) at the mid-position. The summer rainfalls and the snow eventsshow the largest variation in storm continuity at the low elevations whereas for the mixedevents the variation in storm continuity is larger at the upper watershed than at mid-elevation(Fig. 4.4b).The relative storm duration at each station to that of the base station VancouverHarbour has also been studied. The analysis shows that the duration is about twice at the mid-position and at the upper watershed (Fig. 4.5a). Small differences are observed among thedifferent type of events. The variation of these results is larger at the upper watershed forsnow events and summer rainfalls, and at the mid-position for the winter storms and the mixedevents (Fig. 4.4b).Comparing these results with the previous results of the storm precipitation, one cansee that large precipitation at the upper watershed is mainly due to larger duration. A secondreason for the large precipitation at the mid-position of the watershed is the larger averagestorm intensities. Examination of the average storm intensity (storm depth over duration)indicates that the average storm intensity at the mid-position is, on average, about 90 % larger69Chapter 4. STORM PRECIPITATION DISTRIBUTIONthan the average storm intensity at zero elevation (Fig. 4.6a). At the upper watershed theaverage storm rate decreases except for the summer events for which the intensity increases.The convective nature of the precipitation during this period may account for this increase.The variation of the average storm intensity is larger at the mid-position for the winterrainfalls and mixed events, and at the upper watershed for the summer rains and the snowevents (Fig. 4.6b).The above results show that the larger precipitation at the mid-position is due to thelarger storm duration and the larger average storm intensities. However, the average intensitydecreases at the upper watershed, and then levels off.4.3.3 Maximum hourly intensityThe maximum hourly intensity increases from the base station to the mid-position of thewatershed by about 100%, and then either decreases for higher elevation (mixed events),decreases and levels off (winter rainfails) or decreases and then increases at the upperelevation (snow events and summer rainfall) (Fig. 4.7a). The largest variation of themaximum hourly intensity is observed at the upper elevation for all type of events except forthe mixed events for which the largest variation is observed at the mid-position. The snowevents and the summer rainfalls show the largest variation of the maximum hourly intensity(Fig. 4.7b).4.3.4 Relative start timeExamination of the start time of the storms showed that the storms started most of the time atthe mid-position of the watershed and later at the upper and lower watershed (Fig. 4.8). The70Chapter 4. STORM PRECIPITATIONDISTRIBUTIONlarge convergence of the incoming air mass, due to the valley funneling and the orographiclifting increases the condensation so that the storms start first in the middle and upperwatershed.4.4 Time Distribution of StormsThe time distribution of rainfall within a storm is an important characteristic of theprecipitation. There are very few studies that have dealt with the determination of the timedistribution of storms. One classical study is that of Huff (1967) who analyzed 261 stormsfrom a rain gage network covering a 1,037 km2 area in Illinois. From these data, Huffdeveloped a method of characterizing temporal distributions of storm precipitation.Recognizing the variability of hyetographs, Huff expressed temporal distributions ofprecipitation as isopleths of probabilities of dimensionless accumulated storm depths anddurations. He also categorized the storms by the quartile of the storm having the maximumprecipitation. These curves are known as \u201cHuff curves\u201d and have been used for designhyetographs inputs to hydrologic models (Terstriep and Stall, 1974; U.S.D.A., 1980; Bontaand Rao, 1992; Muzilc, 1993). Bonta and Rao (1989) regionalized the Huff curves anddeveloped dimensionless hyetographs for Ohio, Illinois and Texas, and studied the similaritiesand differences between these curves. They found that the Huff curves may be usedthroughout the Midwestern U.S.A. In an earlier paper, Bonta and Rao (1987) concluded thatthe sampling interval of precipitation data and the method identifying storms have only minoreffects on the development of the Huff curves. On the other hand they found that the seasonof year exerts a significant effect on the Huff curves.Coastal British Columbia and the greater region of the coastal Pacific Northwest havea totally different climate from that of the Midwestern U.S.A and therefore probably have71Chapter 4. STORM PRECIPITATIONDISTRIBUTIONdifferent precipitation time distribution curves. In this part of the thesis the 175 stormscollected for seven years (1983-1990) in the Seymour River watershed will be used for theanalysis and development of the time distribution of storms and the examination of the factorsaffecting the storm hyetographs.4.4.1 Research ProcedureThe procedure used by Huff (1967) is adopted for this study. The hyetographs of the 175storms of variable duration will be used to determine the dimensionless hyetographs. Thesedimensionless hyetographs will then be used to express the temporal distribution of storms asprobability distributions (Fig. 4.9). The 10% distribution means that 10% of the storms havea time distribution above this curve. In many cases a median time distribution will be mostuseful, but in others an extreme type of storm distribution (10% or 90%), may be usedbecause such a distribution might maximize the runoff.The only difference between the present study and Huffs work is that the empiricalprobability curves will not be classified by the quartile of the storm having the heaviestprecipitation. Huffs data from Illinois consisted of about 67% thunderstorm rainfall, so that itwas crucial for Huff to categorize the storms by quartile of maximum precipitation, becausethe large intensities during the periods of heavy precipitation have a major effect on thegeneration of the flood runoff. In coastal British Columbia most of the precipitation, as hasbeen discussed in Chapter 2, is produced, even during the summer, by frontal systems. Thisfrontal precipitation is characterized by the small to medium intensities and the long durationresulting in more uniform precipitation pattern, and therefore the classification of the stormsby quartile of heaviest precipitation is rendered unnecessary.72Chapter 4. STORM PRECIPITATION DISTRJB UTIONThe developed curves were compared both visually and statistically. The statisticaltest is by applying the Kolmogorov-Smyrnov (KS) two-sample test (Haan, 1978). Thisprocedure tests for significant differences between two independent cumulative frequencydistributions.The analysis of the storm precipitation has been performed for the five stations usedand for the four types of events. The objective is to evaluate, first, the effect of the elevationand then to identify any changes in the time distribution of the storms with elevation. The50%, 10% and 90% time probability curves for all the stations in the study watershed will becompared.Secondly, the effect of the event type on the time distribution of the storms will beexamined comparing curves at the station 25B at 762 m elevation. These curves have beendeveloped for each type of event, according to their earlier classification into October-Marchrainfall, April-September rainfall, snowfall, and mixed events of rain and snow. Again thetime probability distribution curves 50%, 10% and 90% will be compared.The third test will be to examine the effect of storm duration and storm precipitationdepth on the storm time distribution. This test is critical for the identification of anysignificant changes of the hyetographs with storm duration and precipitation depth and it willbe performed at station S-i.Finally, data collected at three other coastal British Columbia stations have beenanalyzed and their time probability distribution curves have been developed. These resultswill be compared with the data from the Seymour River watershed. Large differencesbetween the various sets of curves would indicate that the developed curves for the SeymourRiver watershed cannot be regionalized over large areas, but on the other hand, similaritybetween the curves developed for the Seymour River watershed and the curves for the other73Chapter 4. STORM PRECIPITATIONDISTRIBUTIONthree coastal British Columbia stations would indicate that they could be used for hydrologicdesign in the whole region of the coastal British Columbia.4.4.2 ResultsThe time distribution of the analyzed 175 storms was determined for all stations in the studywatershed. Figure 4.10 shows the comparison of the 10%, 50%, and 90% curves for all thefive stations. Visual examination of these curves shows that there is no large differencebetween the developed curves except for the 90% curve for the Vancouver Harbour (Fig.4.10). This curve deviates from the rest of the curves. However, the KS test showed nosignificant differences between any of the curves from any station at the 5% level. Hence, theelevation and the topography exert a large effect on the storm precipitation and duration butonly affect the storm time distribution to a very small degree.After classifying the storms into the different type of events, the time distributionprobability curves were determined for each station. Very few storms at the lower elevationswere either snowfalls or mixed events. For example, all the events in Vancouver Harbourwere categorized as rainfall events whereas only seven events were snow storms and fourwere mixed snow and rain events at station S-i at 260 m elevation. On the other hand, atstation 25B at 762 m elevation forty-four storms were classified as snow storms, 35 events asmixed rain and snow events and the remaining events were rain storms. For this reason theeffect of the different type of events on the storm time distribution is examined by using thedeveloped curves at station 25B (Fig. 4.11). Visual comparison of the 10%, 50%, and 90%curves for the various types of events indicates very small differences among the curves forthe various types of the events. Furthermore, the KS test showed no significant differencesbetween any of the curves of the four types of the events at the 5% level, so that the effect of74Chapter 4. STORM PRECIPITATION DISTRIBUTIONthe type of the events is minimal on the time distribution of the storms. This probablyhappens because similar storms during the winter produce the various types of the events.Moreover, it should be mentioned that it is the largest storms and the storms that cover thewhole watershed that have been analyzed and this criterion might have excluded the rareconvective storms that occur only at the upper watershed. Hence, the larger summerrainstorms have a similar time distribution to that of the winter storms. This is also evidentfrom the comparison of the time distribution of the winter and summer rainstorms at allstations.It is important to examine whether the time distribution of storms is affected by thestorm depth and duration. For this reason, the storms at station S-i are categorized intogroups of storms having duration smaller than 24 hours (65 storms), between 24 hours and 48hours (65 storms), and larger than 48 hours (45 storms). Examination of the 10%, 50%, and90% curves shows that the storm duration affects the time distribution of the storms only to avery small degree (Fig. 4.12). Application of the KS test, also, shows that this variation is notstatistically significant at the 5% level.In a similar way the storms are classified according to the total storm precipitation intogroups of storms having storm precipitation smaller than 50.8 mm (78 storms), between 50.8mm and 101.6 mm (52 storms), and larger than 101.6 mm (45 storms). Visual examination ofthe median and the more extreme time distribution probability curves indicates that the stormtime distribution is not affected by the total storm precipitation (Fig. 4.13). Furthermore, theKS test showed no significant differences between the various time probability distributioncurves of any duration and storm precipitation group at the 5% level.The above results suggest that one set of time probability distribution curves, derivedfrom all 175 storms at all seven stations, can be used. In the next paragraphs this average timedistribution probability curves will be used.75Chapter 4. STORM PRECIPITATION DISTRIBUTIONIt is important that the storm time probability curves developed in this study should becompared with the time distribution of storms from other areas in the same climatic region.For this reason data from three stations located in different areas of coastal British Columbiawere used to develop time distribution probability curves. Similarities between the developedcurves for the Seymour River watershed and the curves for the other three stations would thenindicate that the Seymour River watershed curves could be used for hydrologic design in areasof the greater climatic region. The stations used for this analysis are the Carnation Creek CDFstation, the Courtenay Puntledge BCHP station, and the Kitimat station. These stations areA.E.S. stations and are located at different areas of the coastal British Columbia (Fig. 2.1).Because of the different microclimates of the areas of the three stations, different criteria wereused for the selection of the storms analyzed (Table 4.1). The number of years of record isdifferent for these stations and so is the number of the events used for the analysis. However,it is believed that these data depict the storm distribution pattern over coastal BritishColumbia.76Table4.1.CharacteristicsofthecoastalBritishColumbiastationswhosedataanalyzedMinimumSamplingInterstormMaximumUsualDurationPenodNumberDepth(mm)Interval(his)Interval(his)Duration(his)Range(his)of RecordofStormCarnationCreekCDF351611920-401975-19861700CourtenayPuntledgeBCHP20168012-301964-1991334Kltimat30168620401979-1991128Chapter 4. STORM PRECIPITATION DISTRIBUTIONThe time distribution probability curves developed from the data from the threestations and the average curves developed for the Seymour River watershed are compared inFigure 4.14. The comparison shows that the time probability distribution curves of the threeBritish Columbia stations are similar. Furthermore, these curves are similar to the averageSeymour curves shape. Especially, the 90% curves have a similar shape and show very smalldeviation. There is larger deviation between the 10% and the 90% curves but the KS testshowed that this variation is not significant at the 5% level.The above results indicate that the average curves for the Seymour River watershedcould probably be used for the distribution of design storm throughout the coastal BritishColumbia. However, the differences that do exist from one area to another need to beevaluated by using these curves in watershed models. If these differences in the timedistribution probability curves do not significantly affect the simulation of the watershedresponse, then it is reasonable to use the time probability distribution curves developed in thisstudy for the hydrologic design in the whole region.4.5. Antecedent PrecipitationThe antecedent precipitation is a traditional hydrologic index of the soil moisture conditions ina watershed. The soil moisture storage is of particular interest to the hydrologist especiallywhen dealing with mountainous and rural watersheds. The impervious area in both of thesewatersheds is small and the response of the watershed to the precipitation input is controlledby the soil moisture storage. For this reason, the data from the Seymour River watershed havebeen analyzed to obtain the probability estimates of the magnitude of rainfall for periods of 1,2, 3, and 5 days before the occurrence of the storm. The analysis has been done separately forthe October-to-March (winter) storms and the April-to-September (summer) storms.78Chapter 4. STORM PRECIPITATION DISTRIBUTIONFigure 4.15 shows the probability curves of the antecedent precipitation of severaldays at station S-i. According to the analysis the antecedent precipitation is high at station 5-1 especially during the winter months. During the summer period the antecedent precipitationprobability curves of several days become more uniform and the antecedent precipitationsignificantly decreases compared to the winter period. For example, there is 50% probabilitythat the 1-day antecedent precipitation will be larger than 10 mm during the months fromOctober to March, whereas there is only 15% probability that the one-day antecedentprecipitation will be larger than 10 mm during the months from April to September.The antecedent precipitation probability curves at three sites in the study watershed isalso compared (Fig. 4.16). It has been assumed that the station Vancouver Harbour, S-i, and25B represent the lower, middle, and the upper watershed, respectively. This analysis showedthat the antecedent precipitation during the October-to-March period follows a spatial patternsimilar to that of the storm precipitation presented earlier in this Chapter. The antecedentprecipitation increases up to middle watershed (station S-i), and then decreases at the upperwatershed (station 25B). However, the antecedent probability curves converge for the lowprobability levels and similar conditions exist over the middle and upper watershed (Fig.4.16a).The examination of the antecedent precipitation probability curves during the April toSeptember period showed that the soil moisture conditions are more uniform for the wholewatershed and for all the probability levels (Fig. 4. l6b).Even though the antecedent conditions could vary over the whole region of coastalBritish Columbia, the results of this section could be used as a first approximation in absenceof measured data.79Chapter 4. STORM PRECIPITATION DISTRIBUTION4.6 SummaryThe above results show that the topography of the Seymour River watershed plays a verysignificant role in the distribution of precipitation. The largest precipitation is observed at themid-position of the watershed. At this position the valley orientation changes to a northwest-southeast direction. The increased convergence of the incoming air mass produces largeprecipitation, about 200% larger than the zero elevation precipitation. This increase, onaverage, is due to the larger average storm intensities combined with the larger duration.After this middle position the precipitation becomes more uniform at the upper watershed andthe significance of the average storm intensities diminishes so that the larger duration isresponsible for the large precipitation amounts.The precipitation in the coastal British Columbia is generated mainly by strong frontalsystems coming from a southwest direction. This frontal precipitation is characterized by thesmall to moderate intensities and long duration. As a result, the median time distribution ofall types of storms is found to be linear. The time distribution of storms is not affected by theelevation, the type of the event, the storm depth and duration. Analysis of the precipitationrecords of three stations in coastal British Columbia showed that the time probabilitydistribution curves found for the Seymour River watershed may be used in other areas of theregion.The antecedent precipitation of several days has been examined. The results showedthat the antecedent precipitation is relatively high for the whole watershed. However, itincreases with elevation especially in the winter period and for low probability levels. Insummer the antecedent precipitation is affected less by the elevation and drier conditionsprevail.80Chapter 4. STORM PRECIPITATION DISTRIBUTIONThe results of this study should be tested over the same climatic region in order togeneralize them and test the transferability of the findings. Such work has already been donefor the time distribution but not for the spatial distribution of the precipitation. In coastalBritish Columbia there are 96 recording gauges across an area of about 210,000 km2. Thesparcity of the data precludes the examination of the distribution of precipitation in spacewithin a hydrologic unit such as a watershed, and for a short-time scale, such as a stormperiod. This study indicates that the storm precipitation has a similar distribution pattern tothat of the annual and seasonal precipitation. In Chapter 3 it has been shown that the annualand seasonal precipitation distribution across the coastal British Columbia is similar to thatfound in the Seymour River watershed, increasing with elevation up to 400-800 m and theneither leveling off or decreasing at the upper elevations. In light of the findings presented inthis Chapter, it can be assumed that the storm precipitation follows a similar pattern to that ofthe annual precipitation. This distribution can be used as a first approximation in the absenceof measurements in the greater climatic region of coastal British Columbia.81Chapter 4. STORM PRECIPITATION DISTRIBUTION17z015140C)12D 1110DzUI 7I\u2014U0I\u2014 4zUIC)UIC-01716151413121110z 9UID 80UIU-543210Fig. 4.1 a) Monthly distribution of the average annual precipitation at station S-Iand (b) Monthly distribution of the 175 storms analyzed.(a)JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMONTHS82Chapter 4. STORM PRECIPITATION DISTRIBUTION3.8zo 3.63.4Ia-3.20wa.. 2.8z02.42.2w91.81.6Q1.4I1.2I0.90.8zQ0.7O.60.50.400.3U0.20.10Fig. 4.2 a) Precipitation ratio to base station (Vancouver Harbour) for variousstations and types of events and (b) its coefficient of variation.0 200 400 600 8000 200 400 600 800ELEVATION (m)83DISTANCE(km)10.90.80.70.60.5It00___RAINOCTMM.INSEP-APRSNOWMIXEDRNN.SNOW0.10048121620242832Fig.4.3.SpatialcorrelationfunctiOnSforthevarioUStypesofstorms.Chapter 4. STORM PRECIPITATION DISTRIBUTION1z0z0U0LUC)UULU0C-)0.90.80.70.60.50.40.30.20.1010.90.80.70.60.50.40.30.2 -0.100 200 400 600 800(b)RAINFALL OCT-MARRAINFALLAPR-SEPSNOWMbCURE RAIN-SNOW.\u2014. S..S. SS.. *I I I I I I0 200 400 600 800ELEVATION (m)Fig. 4.4. (a) Storm continuity at various elevations and types of storms(b) Coefficient of variation of storm continuity.85Chapter 4. STORM PRECIPITATION DISTRIBUTION2.22.12o 1.91 1.8z 1.71.61.51.41.31.21.1110.9zO 0.8> 0.6U0 05Ui 0.400.3Ui8 0.20.10ELEVATION (m)Fig. 4.5. (a) Storm duration ratio to base station for various elevationsand types of storms (b) Coefficient of variation of storm duration ratio.0 200 400 600 8000 200 400 600 80086Chapter 4. STORM PRECIPITATION DISTRIBUTION2.121.91.81.7Q 1.6I\u20141.51.41.31.21.1I0.80.7zQ 0.60.5o 04Ui0.3LLUUio 0.2C.)0.10ELEVATION (m)Fig. 4.6. (a) Ratio of the average storm intensity to base station for variouselevations and types of storms and (b) its coefficient of variation.0 200 400 600 80087Chapter 4. STORM PRECIPITATION DISTRiBUTION21.91.81.7o 1.61.51.41.31.21.11.71.61.51.4zo 1.31.21.1LI0I- 0.8zw 0.70.6U-0.5o 0.40.30.20.10Fig. 4.7. a) Ratio of the maximum hourly intensity to base station for variouselevations and types of storms and (b) its coefficient of variation.882.10 200 400 600 8000 200 400 600 800ELEVATION (m)0\u20141-2-3-5-6800:I... 0 w I\u201400-40200400600ELEVATION(m)Fig.4.8.Stormrelativestarttimetothebasestationatdifferentelevationsandtypeof storm.06CUMULATIVEPERCENTOFSTORMPRECIPITATION-cz.aoo00000000,,0C,0\u2022.m.-UDC) m0.-I0 ,<,1C)\u201c_I00 CD0) \u2014o0r000CD0NOLLfliiisiaNOLLV1IJI31dIAIIOISA?1dV1f3z 0 I 0 C) Lii 0100 90 80 70 60 50 40 30 20 10 0100CUMULATIVEPERCENTOFSTORMDURATIONFig.4.10.Comparisonofthetimedistributionprobabilitycurvesfordifferent stationsandelevationsintheSeymourRiverwatershed020406080z 0 I. a 0 w a: a a: P U) U 0 I. z w 0 a: Ui a Ui100 90 80 70 60 50 40 30 20 10 020406080100CUMULATIVEPERCENTOFSTORMDURATiONFig.4.11.Comparisonof thetimedistributionprobabilitycurvesfordifferenttypeofeventsatthestation25BintheSeymour Riverwatershed.01009O8Ow 7060U) 5oI. 4Ow a >iii3020C)10 0100CUMULATIVEPERCENTOFSTORMDURATIONFig.4.12.Theeffectofthestormdurationonthetimeprobabilitydistributioncurves(StationS-i)020406080z 0 I\u2014 a C) Lii 0100 90 80 70 60 50 40 30 20 10 0100CUMULATIVEPERCENTOFSTORMDURATIONFig.4.13.Theeffectofthestormprecipitationonthetimeprobabilitydistributioncurves(StationS-i)020406080100Z900 8070Ui 0 600 -500 I\u2014 z Ui 40U\u2019Ui 030Ui20010 0020406080100COMULATIVEPERCENTOFSTORMDURATiONFig.4.14.Comparisonof thetimeprobabilitydistributioncurvesfor SeymourRiverwatershedandthreeCoastalBritishColumbiastations.w0zw\u2014\u201c \\SOS %_ \u20140 I I0 20 40 60 80 100 120 140 160 180 200ANTECEDENT PRECIPITATION (mm)Fig. 4.15. Probability of equality or exceedance of antecedent precipitationat station S-i for (a) October to March storms and (b) April to September stormsChapter 4. STORM PRECIPITATION DISTRIBUTION10.90.80.70.60.50.40.30.20.1010.90.80.70.60.50.40.30.20.1(b)1-DAY2-DAY3-DAY5-DAY96Chapter 4. STORM PRECIPITATIONDISTRJBUTIONwC)zwUiC.)0-JDaUiLL.0-J0UiC)zUi1\u20140.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.10(a)3-DAY VANCOUVER HARBOUR3-DAYS-I3-DAY25B\\\u2018 \\. \\\\ \\.. \u201c\\ \\ S\\ \u2022\\ 5S*\\ \\ \u2018S\\ \\ 5\u2019N,\u2022\\ \\. \u2018S\\ %. \u2018S.5\u2019. S5\u2019-\u2018S..I I I I I I ._0 20 40 60 80 100 120 140 160 180 2000.90.80.70.60.50.40.30.20.10 0 20 40 60 80 100 120 140 160 180 200ANTECEDENT PRECIPITATION (mm)Fig. 4.16. Comparison of the probability of equality or exceedence of the antecedentprecipitation for different elevations for (a) winter and (b) summer.97CHAPTER 524-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA5.1 IntroductionThe estimation of peak flows is necessary for the design of hydrotechnical structures. Ifstreamfiow data are available, a conventional flood frequency technique is applied.Unfortunately there are many streams for which measurements are not available and in thesecases many different approaches can be used as will be presented in Chapter 8 of the thesis.One of these methods is the event-based simulation of individual large events, in which adesign storm can be derived and used as input to a watershed model for the estimation of thestorm runoff hydrograph, which provides estimates of the volume of runoff and the peak flow.The design storm considerations include the return period, the total storm depth, the stormduration, the storm temporal distribution, the storm spatial characteristics, the time responseof the watershed and the antecedent soil moisture state of the watershed.The return period of the storm is selected on the basis of minimizing the cost orassuring a certain level of protection of the hydraulic structure, and consequently of thecommunity. In Canada, the level of protection is determined by the Provinces and depends onthe type of structures (Watt et al., 1989). The total precipitation depth at a point is a functionof the return period and the storm duration, which is linked to the time of concentration of thewatershed. The variation of the rainfall intensity during the storm is an important factor indetermining the timing and the magnitude of the peak flows. In addition, the spatial coverageof the storm influences the runoff generation and is especially important for larger basins.In earlier studies in Canada the focus was on the derivation of the design storm forChapter 5. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAurban watersheds, usually 1-hour to 12-hour storm (Hogg, 1980; Hogg, 1982; Marsalek andWatt, 1984; Watt et aL, 1986). This study is concentrated on the derivation and study of the24-hour design storm for coastal British Columbia mountainous and rural watersheds.This choice of the 24-hour storm duration has been based on many factors. Firstly, theprecipitation in the coastal British Columbia is generated mainly by long duration frontalstorms as it has been discussed in Chapter 2. Most of these storms have a duration of about aday as it has been shown from the regional data analyzed in Chapter 4. Secondly, theresponse of small and medium mountainous watersheds is in the order of several hours so thata long duration storm is required for the generation of peak flows. One might think that ashort duration storm may be more adequate for the estimation of peak flow from small steepwatersheds of the region. Extensive research in the Jamieson Creek watershed, a small steepwatershed which will be used later in the analysis, showed that for rain storms the time lagvaried between 5.5 hours to 15 hours with an average of about 8.5 hours (Cheng, 1976). Forthe most intense and severe storms the time lag decreases down to 2-2.5 hours (Loukas, 1991).These results show that a design storm of longer duration from that of the time lag, like a 6-hour, 12-hour or 24-hour storm could be adequate for the simulation of the peak flow.However, simulation of the peak flows from coastal British Columbia watersheds showed thatthe 24-hour storm is more suitable, especially for the larger return period floods, as will beshown in Chapter 8 of the thesis (Fig. 8.3). Furthermore, the choice of the 24-hour stormduration is a pragmatic one. Of the 269 precipitation stations located in the coastal BritishColumbia, an area of about 210,000 km2. 173 are storage gauges which are used to measurethe daily precipitation. These stations have longer records than the recording stations (Fig.5.1), which implies more reliable frequency analysis, since the use of the 24-hour designstorm can expand the usable data both in space and time resulting in better estimation of floodrunoff from ungauged watersheds.99ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIASince the storm duration of 24 hours is accepted to be adequate for small and mediummountainous and rural watersheds, the time distribution of the storm and its variation in spaceare the most important parameters of the design storm.The objective of this Chapter is to present the results of the development of a 24-hourdesign storm using data from the Seymour River watershed. Important parts of this study willbe, firstly, to examine whether the temporal distribution changes with elevation, secondly, toidentify the spatial distribution of the precipitation, thirdly, to compare the developed stormwith other synthetic storms used in the hydrologic design, and finally to investigate thepossibility of transferring the results in other areas of coastal British Columbia. It should benoted that the scarcity of both precipitation and streamfiow data in this mountainous regionrestricts the application of conventional flood frequency analysis and therefore the use of thedesign storm concept along with rainfall-runoff simulation is one of the methods used in thepractical application of hydrology. Also, the study of the 24-hour design storm is differentfrom the analysis of the storm precipitation presented in Chapter 4, since in this Chapter onlythe extreme rainfall events with duration of 24 hours will be analyzed.5.2 Data Sets and Method of AnalysisData from five precipitation recording gauges in the Seymour River watershed will beanalyzed. The data sets from the stations Vancouver Harbour, S-l, bA, l4A and 25B areused. The characteristics of the stations have been shown in Table 2.2 and their location hasbeen presented in Figure 2.3 of Chapter 2. The stations cover an elevation range of about 760m. The Vancouver Harbour station is the sea-level station and the 25B station is the secondhighest station of the Seymour watershed located at 762 m elevation (Table 2.2).The maximum 24-hour rainfall usually occurs in the period from October to January100ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA(Fig. 5.2) when precipitation is generated by strong frontal systems coming from the NorthPacific Ocean. As has been mentioned in Chapter 4, the precipitation during these systems ischaracterized by long duration and small to moderate intensities.The selection criteria of the storms have been chosen to identify the 2 or 3 largerstorms per year for each station. Hourly rain data were used for the analysis of the storms. The24-hour storms selected for the analysis have rainfall depths larger than 55 mm for VancouverHarbour and larger than 90 mm at the other stations. Under these criteria, 21 storms for theperiod 1976-1990 for the Vancouver Harbour station, 23 storms for the period 1984-1990 forthe station s-i, 32 storms for the station 1OA for the period 1976-1990, 28 storms for 14A forthe period 1980-1990, and 16 storms for the station 25B for the period 1980-1990 wereselected.Analysis of the data for the study of the time distribution is achieved by a method verysimilar to the one presented by Huff (1967) and used for the analysis of the stormprecipitation in Chapter 4. To compare different storms, rainfall for each event was expressedas the cumulative percentage of the total twenty-four-hour rainfall for twenty-four equal timeincrements through the storm. The resulting values were then used to calculate the timeprobability distributions which provide quantitative measures of both interstorm variabifityand the general characteristics of the time sequence of the rainfall. For the few storms whichlasted less than 24 hours, the residual time increments were entered as zero rainfall tocomplete the fixed 24-hour duration event.The developed time probability curves will be compared both visually and statistically.The statistical test is the Kolmogorov-Smirnov (KS). two sample test (Haan, 1977), whichtests for significant differences between two independent cumulative frequency distributions.The spatial distribution of the maximum 24-hour storms will be analyzed in the studywatershed, and then the application of this distribution to other areas of the coastal British101Chapter 5. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAColumbia region will be investigated in the next paragraphs.5.3 Time DistributionApplying the above mentioned method of analysis, time probability distributions weredeveloped for each of the five stations, and an example is shown in Figure 5.3 for station S-i.The percentages are defined such that, for example, for the thirty percent time distributioncurve, thirty percent of the storms will have a time distribution above the curve. The timeprobability distributions of ten, thirty, fifty, seventy, and ninety percent are shown in Figure5.3.It is important to know how the time distribution of the storm varies at differentelevations. A detailed data base is difficult to find in coastal British Columbia. However forthe Seymour River watershed data are available at five stations for an elevation range of about760 m. The fifty, ten and ninety percent time probability distributions for different elevationsare compared in Figure 5.4. From this figure it is observed that the storm time distributionwith elevation does not vary significantly and even the extreme time probability curves of tenand ninety percent have similar patterns. The largest deviation of the results is observed forthe 90% curves. However, application of the KS test shows no significant differences at the5% level between the curves from the stations at various elevations. Because of the smalldifferences of the storm time distribution with elevation, average time probability distributionshave been developed using the 120 storm distributions from all stations in the study watershed(Fig. 5.5).It is important to examine whether the 24-hour storm time distribution for theSeymour River watershed can be transferred to other areas of coastal British Columbia.Similarities between the developed time probability curves for the Seymour River watershed102ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAand the curves developed in other studies for the same region or by using regional data wouldthen indicate that the Seymour River watershed curves could be used for hydrologic design inother areas of the same climatic region. For this reason the time probability distributioncurves developed for the Seymour River watershed wifi be compared firstly with the stormtime distributions from other studies in the region and then with the time probability curvesusing other coastal British Columbia data.Melone (1986) analyzed the time distribution of the largest 24-hour storm of recordobserved at each of 58 recording stations across coastal British Columbia, and developed thetime probability distributions of the 24-hour storm for the 58 stations. In Figure 5.6 theresults of Melone\u2019s work are compared with the average time probability distributions for theSeymour River watershed. It is evident from this comparison that the results of this studycover the time distribution of the extreme 24-hour storms in coastal British Columbia stations.Only the ten percent curves deviate, and a possible explanation for this deviation may be thatMelone analyzed only the largest 24-hour storm for each of the 58 stations whereas in thepresent study a large number of storms have been analyzed for each station in the studywatershed. However, the general pattern of the rain distribution, at least during the mostextreme storms, is similar to the pattern observed in the study watershed. In addition, thestatistical KS test showed that there are no significant differences between any of the curves atthe 5% level.The results of this study were also compared to the results that Hogg (1980) reported.Hogg analyzed 119 12-hour events from coastal British Columbia stations. Hogg suggested(Hogg, personal communication) that the time distribution of the 24-hour design storm shouldbe similar to the 12-hour storm. Figure 5.7 shows the comparison of Hogg\u2019s results with theresults of this study. It is evident from this figure that the 24-hour design storm developed inthis study has a similar time distribution to the 12-hour design storm developed by Hogg for103ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAthe British Columbia coast. Also, the statistical KS test showed that there are no significantdifferences between any of the curves at 5% level.The findings of this study are also compared with the time distribution of the extreme24-hour storms from other areas of the same climatic region. Data sets from three stationslocated in different areas of coastal British Columbia were used to develop time probabilitydistribution curves. The stations used for this analysis are the Carnation Creek CDF station,the Courtenay Puntledge BCHP station, and the Kitimat station (Fig. 2.1) which has been usedin Chapter 4 for the comparison of the storm precipitation time distribution. Because of thedifferent microclimates of the areas of the three stations, different criteria were used for theselection of the 24-hour extreme storms analyzed (Table 5.1). The number of years of recordis different for these stations and so is the number of the events used for the analysis.Table 5.1. Characteristics of the coastal British Columbia station used in the analysis of the24-hour extreme rainfall time distribution.Greatest Mean Annual Minimum Period of NumberStation Name 24-hour 24-hour Storm Depth Record ofRainfall Depth (mm) (mm) Storms(mm)Carnation Creek CDF 230 96.9 70 1975-1986 50CourtenayPuntledge 124 69.9 55 1964-1991 50Kitimat 123 92.7 55 1979-1991 31104ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAThe time distribution probability curves developed from the data from the threestations and the average curves developed for the Seymour River watershed are compared inFigure 5.8. The comparison shows that the curves of the three British Columbia stations aresimilar. Furthermore, these curves are similar to the average Seymour curve shapes. Thevariability that exists between the time probability curves may be explained by the differencesbetween the microclimates of each site. However, the KS test showed no significantdifferences between these curves at the 5% level. The above three comparisons suggest thatthe results of the study can be transferred to other regions of coastal British Columbia withoutloss of accuracy.The results of this present work have been compared with some other design stormsused by engineers in every day practice. Four such design storms were considered: the SoilConservation Service Type I and Soil Conservation Service Type IA storms (U.S.D.A.,1986), the Hershfield storm (Hershfield, 1962) and the storms that can be developed usingIntensity-Duration-Frequency (IDF) curves, the so-called Alternating Block Method (Chow etal., 1988). The first two design storms were developed by the U.S. Department of AgricultureSoil Conservation Service (SCS). The SCS developed five 24-hour duration storms, and thestorms called Type I and Type IA were developed for use on the coastal side of the SierraNevada and Cascade mountains of Oregon, Washington and Northern California, and thecoastal regions of Alaska. These hyetographs were derived from information presented byHershfield (1961), by Miller, Frederick, and Tracey (1973) and from additional storm data.These two storms, SCS Type I and IA, are extensively used in coastal British Columbia sincethe province is located within the greater climatic region for which the storms are designed.The Hershfield storm (Hershfield, 1962) was developed by analyzing data from 50widely separated stations with different rainfall regimes across the U.S.A. and an averagecurve was prepared for all storm durations.105ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAThe Alternating Block Method is a popular technique among practicing engineers.This method is a simple way of developing a design hyetograph from Intensity-Duration-Frequency curves. One design hyetograph can be developed for each return period and eachstorm duration.These various design storms are compared in Figure 5.9 and the comparison indicatesa significantly different rainfall pattern than the observed values. The Alternating BlockMethod synthetic hyetograph represents a totally different time distribution pattern from theobserved. The curve SCS Type IA shows better agreement with the 10% curve developed inthis study. It is evident from the above comparison that most of the synthetic hyetographsrepresent storms with intense periods of rain. On the other hand, the observed pattern is moreuniform and does not contain intense bursts of rain.It is important, however, to identify whether the variations observed between thesynthetic hyetographs and the derived time distributions significantly affect the simulation ofthe runoff. This analysis will be presented at the end of the Chapter after the presentation ofthe spatial distribution of the extreme 24-hour storms and the analysis of the antecedentrainfall.5.4 Spatial DistributionAs indicated from the results of the analysis of mean annual precipitation in Chapter 3, themain spatial variation of the precipitation is variation with elevation for medium to smallmountainous watersheds. In this part of the Chapter the spatial distribution of the designstorm with elevation will be examined.In the Seymour River watershed the 24-hour maximum storm data have been analyzedand the Extreme Value I (Gumbel) probability distribution has been fitted to the data. The106ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAresults (Fig. 5.10) show that, for all the return periods, the rainfall increases up to the middleposition of the watershed (station S-i, at 260 m), and then abruptly decreases (station bA, at293 m), before a further slight increase and leveling off at the upper elevations (Fig. 5.10).This particular rain distribution is similar to the distribution of the mean annual precipitationand mean storm precipitation in the study watershed. Both the mean annual precipitation andthe extreme 24-hour storms increase by an average factor of about 2.5 between the VancouverHarbour and S-l stations and then decrease to a factor of 1.7 at station bOA and then increaseand level off to a factor of 2.0 at station 25B. The reason for the abrupt decrease of the rainafter the S-b station is the topography of the area. At the position of station S-i the SeymourRiver valley turns to the northwest and the resulting increased convergence of the incomingair mass generates large precipitation at this middle position. Unfortunately, there are notenough recording stations in the area in order to compare the observed rainfall distribution inthe study watershed with the precipitation distribution in adjacent watersheds. However, theanalysis of the long term annual, seasonal, and monthly precipitation accumulations from thenearby Capilano watershed, presented in Chapter 3, showed that the precipitation distributionin Capilano watershed is similar to that of the Seymour watershed except for the largedecrease after the middle of the watershed. Hence, it seems that the rainfall in the study areaincreases for the first topographical rise and then either levels off or even decreases. Thegenerality of this conclusion for the coastal British Columbia will be examined in the nextparagraphs.Three aspects will be examined. Firstly, it is important to examine the spatialdistribution of rainfall over the watershed during the extreme events. The results shown inFigure 5.11 are based on all the extreme events and they have not necessarily occurred at thesame time throughout the watershed. However, the analysis of the storm precipitation in thestudy watershed have shown that the rainfall during a single storm also has a similar107ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAdistribution. Furthermore, a study of the spatial and temporal disthbution of extremehistorical storms will be presented in Chapter 6 and it will be shown that during an individualstorm the precipitation is distributed with a similar pattern to that of the storm and meanannual precipitation found in Chapters 3 and 4. It is also very important to note that the stormrainfall increases and decreases, on average, at a similar rate as the mean annual precipitation.Secondly, it is important to evaluate whether the spatial distribution of the extremeprecipitation observed in the Seymour River watershed is transferable to the coastal BritishColumbia region. Unfortunately, there are not enough recording precipitation stations in theregion to analyze the spatial distribution of the short term precipitation. However, there aremore storage gauges which could give a good indication of the spatial distribution of thelonger term precipitation. The analysis presented in Chapter 3 has shown that the meanannual precipitation in the coastal Pacific Northwest increases up to an elevation of 400-800m, and then either levels off or even decreases at higher elevations. Hence, the long-termprecipitation in the greater region has a spatial distribution pattern with elevation similar to theone observed in the Seymour River watershed except that there is not such a large decrease ofthe rainfall after the middle position of the watershed. This leads to the third question ofwhether the extreme 24-hour storm rainfall is a certain percentage of the mean annualprecipitation. If this is true then the mean annual precipitation could be used as an index ofstorm rainfall. The above hypothesis is probably reasonable because most of the annualprecipitation is caused by the same type of low pressure systems and most of this rainfalloccurs in the fall and winter. This hypothesis will be examined in the next paragraphs.The analyzed extreme 24-hour rainfall data for return periods of 2-, 5-, 10-, 25-, 50-,and 100-year return period and mean annual precipitation were obtained from EnvironmentCanada, Atmospheric Environment Service for sixty-one recording stations across coastalBritish Columbia. Thirty-five stations are located in the southwest mainland coast, thirteen on108ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAthe east of Vancouver Island and the Gulf Islands, six on the west of Vancouver Island, andseven are located on the north mainland coast and Queen Charlotte Islands. The sixty-onestations used in the study are listed in Table Bi in Appendix B.The extreme 24-hour rainfall for the sixty-one British Columbia stations has beenplotted against the station mean annual precipitation and regression analysis has also beenperformed between the extreme 24-hour storm rainfall of the various return periods and themean annual precipitation. The results of this analysis are shown in Appendix B. Figure 5.11presents the results for the 10-year 24-hour rainfall and indicates that this storm is, on average,5.7% of the mean annual precipitation.However, analyses of this type are biased towards southwestern coastal BritishColumbia since most of the recording stations are located in that region. For this reasonseparate analyses have been carried out for the southwest mainland coast, for east VancouverIsland, west Vancouver Island, and the north coast of British Columbia. The results aresummarized in Table 5.2 which lists the mean and the range in percentage of the extreme24-hour precipitation against the mean annual precipitation. There is a significant overlappingbetween the ranges for the various return periods and sub-regions but it can be observed thatthe average value increases as the return period increases. Also from this Table can be seenthat the ratio of the extreme 24-hour rainfall and the mean annual precipitation has itsmaximum value for the east coast of Vancouver Island, the dryer of the four sub-regions andalso, the variation of the ratio is the largest in this sub-region.109ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIATable 5.2. Ratio of the 24-hour rainfall and mean annual precipitation for various coastalsub-regions of British Columbia.Return Southwest East Coast of West Coast of North B.C. CoastPeriod Mainland B.C. Vancouver Vancouver Island(years) Coast (%) Island (%) (%) (%)2 4.1 (3.0-5.3) 5.1 (3.3-7.3) 4.2 (3.3-4.8) 3.8 (3.2-4.3)5 5.1 (3.6-6.5) 6.6 (3.9-10.3) 5.2 (4.3-6.2) 4.9 (4.0-6.1)10 5.8 (4.0-7.5) 7.7 (4.4-12.3) 5.9 (5.0-7.2) 5.7 (4.5-7.4)25 6.7 (4.5-8.6) 8.9 (5.0-14.9) 6.8 (5.8-8.4) 6.6 (5.1-8.9)50 7.4 (4.9-9.8) 9.9 (5.4-16.7) 7.5 (6.4-9.3) 7.3 (5.6-8.9)100 8.0 (5.3-10.9) 10.8 (5.8-18.6) 8.1 (7.0-10.2) 7.9 (6.0-11.2)Mean AnnualPrecipitation 1968 (982-3600) 1122 (619-1656) 2824 (1870-3943) 2050 (1137-3 155)(mm)The above analysis indicates that an estimate of extreme 24-hour rainfall estimate can be madebased on mean annual precipitation at a location. Therefore, it is reasonable to suppose thatthe distribution of long term precipitation with elevation is an index for the distribution ofstorm rainfall. This conclusion is supported by the results found for the Seymour Riverwatershed, as previously discussed. This means that the largest storm rainfall does not increaselinearly with elevation, but increases at the low and middle elevation, and then levels off at thetop elevations.110ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA5.5 Antecedent RainfallThe antecedent rainfall for periods of several days before the design storm is important to thehydrologist, particularly if the problem involves rural or mountainous basins as opposed tohighly impervious urban watersheds. The antecedent rainfall characterizes the soil moistureconditions in the watershed prior to the occurrence of the storm and therefore, controls theresponse of the watershed.Table 5.3 shows the estimates obtained for the antecedent rainfall at three stations inthe Seymour River watershed for five probability levels and for 1- to 5- day periods. It isassumed that the three stations represent the lower, middle and upper watershed (stationsVancouver Harbour, S-l, and 25B, respectively).These results show that the antecedent rainfall increases with elevation. Furthermore,there is a fifty percent probability that the 1-day antecedent rainfall at the middle and upperwatershed will be about 20 mm. This is critical for the generation of the runoff from the steephillslopes of the watershed and shows that there is a high probability that the soil moisturelevels will be high, especially throughout the winter period.The antecedent rainfall statistics may vary considerably in the same climatic region butthe results of this study give a good first approximation.111ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIATable 5.3. Probability distribution of antecedent rainfall (mm) for the maximum 24-hourstorms for various numbers of days.Probability(%) 1-day 2-days 3-days 5-daysVancouver Harbour10 62.0 81.4 93.4 135.330 22.2 43.2 52.4 77.250 7.9 22.9 30.1 44.170 2.8 12.2 17.0 25.290 1.0 6.5 9.7 14.4s-i10 82.2 129.6 158.0 244.430 38.4 66.7 83.2 129.350 17.9 34.3 43.8 68.470 8.4 17.7 23.1 36.290 3.9 9.1 12.2 19.125B10 87.0 154.6 262.9 565.530 43.9 85.8 133.0 242.350 22.1 47.6 67.3 104.070 11.1 26.4 34.0 44.790 5.6 14.7 17.2 19.2112ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA5.6 Simulation of Peak Stream Flow at Jamieson Creek WatershedThe observed time distributions and the synthetic hyetographs presented earlier have beenused as input to a simple watershed model for the calculation of streamfiow runoff. Thestreamfiow data have been taken from the Jamieson Creek watershed. Jamieson Creek is asmall tributary of the Seymour River, and is located in the headwaters of the river system. Thebasin has an area of 2.99 km2, and its elevation ranges from 305 to 1310 m above mean sealevel so that there is a good variation in elevation. Jamieson Creek is characterized by steephillslopes having an average gradient of 48%.Because of the small area of the Jamieson Creek watershed, rainfall data from onestation were considered adequate. The station 25B is located in the middle of the watershed,so that it is assumed to represent the average rainfall over the watershed, and its data havebeen used to estimate the 24-hour rainfall depth. This assumption was confirmed in an earlier,more detailed study (Loukas and Quick, 1993b) in which five stations within the JamiesonCreek watershed were used.The 24-hour rainfall for various return periods has been distributed in time accordingto the observed and synthetic hydrographs and the resulting storms have been used as input toan event based watershed model. The watershed model was developed in a previous studyand it was shown to give good simulation of the watershed response (Loukas, 1991).The watershed model is an event model which uses a linear reservoir routing techniqueand simulates the fast runoff with a series of cascading reservoirs and the slow runoff with onelarge reservoir (Fig. 5.12). The whole process is infiltration controlled using a powerrelationship as:F=If+a.t_b (5.1)113Chapter 5. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAwhere Ps is the rainfall infiltrated and diverted to slow runoff (mm\/h), Ij is the finalinfiltration abstractions (mm\/h), and a and b are constants. The remaining rainfall Pf from thetotal rainfall P is diverted to the stream as fast runoff.In the previous study this model was kept deliberately simple for a first analysis withthe intention of adding more complexity to handle soil moisture, but the model was found toperform well, so that no additional complexity was added to it.An assumption that underlies the application of rainfall-runoff simulation for theestimation of the extreme flows is that the return period of the peak flow is the same as thereturn period of the 24-hour rainfall depth. This assumption is common in the application ofdesign storms for practical purposes, but it has been challenged (Dickinson et al., 1992). Theabove assumption should hold if the watershed is small and the only causative factor of floodsis the extreme rainfalls which occur at certain periods of the year. The annual floods in thewatersheds of coastal British Columbia can be generated by rainfall, rain on snow, and snowmelt events (Melone, 1985). In the case of Jamieson Creek watershed, rainfall and rain onsnow are the dominant flood producing mechanisms. The annual rain generated floods wereidentified and separately analyzed from the rain on snow floods.Generally, there are a number of combinations of watershed conditions, extremestorms of various return periods and time distributions that can produce a flood of a givenreturn period. For example, a 10-year rain storm over dry soil can produce peak flowsignificantly less than a 10-year flood because of the larger abstractions to the soil storage.However, for this study area, analysis of the time of the occurrence of the largest annual24-hour rainfall showed that there is about 75% probability that the maximum 24-hour stormwill occur in the period from October to January (Fig. 5.2), and during this time period thesoil in the study area is wet. More specifically, as already discussed in the previous section,analysis of the antecedent rainfall prior to the extreme events showed that there is fifty percent114ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAchance that the 1-day antecedent rainfall at the upper Seymour River watershed will be largerthan 20 mm. Hence, it has been assumed that the soil is saturated at the beginning of theextreme storm and the abstractions to the soil moisture storage have been set to zero.Using the above assumptions, tests have been made to compare the observed raingenerated peak flows with the simulated flows using the various synthetic and derivedhyetographs. These analyses have been used to generate the 2-, 5-, 10-, 25-, 50-, and 100-yearfloods from the Jamieson Creek watershed. Table 5.4 shows the comparison of the simulatedpeak flows using the various hyetographs with the observed rain generated peak flows for theJamieson Creek watershed. These results indicate that the 10% time probability distributioncurve derived earlier in this Chapter gives results that are close to the observed peak flows.The agreement is better for small return periods. It should be noted that the \u201cobserved\u201d 25, 50,and 100-year floods are extrapolations of the observed flows of 16 years using the fittedExtreme Value I (Gumbel) probability distribution. Hence, it is reasonable to expect highervariation between the simulated and the \u201cobserved\u201d peak flows for the larger return periods.Of the various well known rainfall design storms, only the SCS Type IA curve gavereasonably good results. The other synthetic storms produce larger peak flows than theobserved flows for all the return periods. The Alternate Block Method hyetograph producesthe highest peaks because of the inherent assumption of the method that all the intensities fordurations from 1 to 24 hours will occur in the design storm. Furthermore, the arrangement ofthe intensities in symmetrical fashion around the middle of the storm duration is anotherreason for the over-maximization of the storm and consequently the peak flow. The SCSType I and the Hershfield hyetographs produce higher estimates of the peak flow by about20% and 10%, respectively (Table 5.4).115ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIATable 5.4. Comparison of the simulated peak flows (m3\/sec) using various hyetographs withthe observed flows of Jamieson Creek watershed.Return Period (years)Hyetographused 2 5 10 25 50 10010% curve 7.01 9.10 10.48 12.25 13.52 14.8150% curve 5.10 6.62 7.63 8.90 9.84 10.7890% curve 6.10 7.90 9.12 10.64 11.7 12.81ABM 11.49 14.49 16.69 19.83 21.83 23.79Hershfield 8.11 10.53 12.12 14.14 15.64 17.13SCS Type I 8.73 11.33 13.05 15.22 16.84 18.44SCS Type IA 7.21 9.35 10.77 12.57 13.90 15.23Observed 6.20 8.85 10.60 12.82 14.47 16.10From the derived time distributions only the 10% curve, derived in this study, gives peak flowestimates close to the observed flows. The 50% and the 90% curves produce the lower peakflows of all the hyetographs tested.In addition to the peak flow itself, two other important considerations for theapplication of the design storm are the shape of the resulting hydrograph and the timing of thepeak flow. Figure 5.13 compares the observed and the simulated 10-year flood hydrographsfor the various hyetographs. The SCS Type IA storm and the derived 10% storm gave similarhydrographs to the observed hydrograph. The hydrograph of the 10% storm has higher flowsat the beginning of the event and it peaks later than the SCS Type IA hydrograph. However,116ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAthe hydrograph volumes are similar. The other synthetic hyetographs produce hydrographsdifferent in shape from the observed hydrograph. The hydrograph generated by theAlternating Block Method curve significantly overestimates the peak flow and has a moresymmetrical shape than the other hydrographs. The SCS Type I and Hershfield storms bothgave similar hydrographs but the Hershfield storm produced a more delayed peak flow.Finally, the derived curves of 50% and 90% produce the most delayed and flat hydrographs(Fig. 5.13).The above results show that the design storm derived in this study, using the 10% timeprobability distribution curve, gave the best results. For the other design storms only the SCSType IA storm should be used for the estimation of peak flows from mountainous watershedsin the study area, and gives reasonable results provided that attention is paid to theassumptions discussed at the beginning of this section.5.7 SummaryThe distribution of the extreme 24-hour storms in space and time has been analyzed using therainfall data from the Seymour River watershed. The choice of the 24-hour storm durationwas based on meteorological, hydrological and pragmatic reasons. Firstly, the extreme stormsare strong winter frontal storms which usually have a duration of about a day. Secondly, theresponse of rural and mountainous watersheds in the study area is in the order of several hoursso that a long duration storm should be used as a design storm. Finally, the use of the 24-hourdesign storm expands the usable data both in space and time since there are more and longerdaily records from storage precipitation gauges and only a few recording gauges in coastalBritish Columbia.The analysis showed that the time distribution of the 24-hour storms does not change117ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIAsignificantly over the elevation range of the data in the Seymour River watershed andtherefore, average time probability distributions have been developed. The transferability ofthe derived storm time distributions was tested against the results of other studies whichanalyzed rainfall data from coastal British Columbia and actual data from three coastal BritishColumbia stations. This comparison showed that the 24-hour storm time distributions of thisstudy appear to be transferable to other areas of coastal region of British Columbia.Examination of other synthetic storms revealed that most of these storms have a timedistribution which is characterized by bursts of intense rain which do not appear to beobserved in the Seymour River watershed data or in other data from recording rain gauges inthe region. Application of the derived and synthetic storms to a real watershed showed thatthe 10% time probability curve and the SCS Type IA curve gave good estimation of the peakflow. Furthermore, the shape and the time to peak of the hydrograph is similar for these twostorms. These results justify the application of either the storm hyetograph derived in thisstudy using the 10% curve, or the SCS Type IA hyetograph, for the estimation of peak flow incoastal British Columbia.Study of the extreme 24-hour storm rainfall in the Seymour River watershed indicatesthat the storm rainfall increases up to the middle position of the basin, and then decreases, andlevels off at the top elevations. It is very important to note that the extreme 24-hour rainfall ishighly correlated with mean annual precipitation. Analysis of the extreme 24-hour rainfall forthe four sub-regions of the coastal British Columbia showed that the 24-hour storm depth is acertain percentage of the mean annual precipitation and agrees with earlier findings by Melone(1986). Therefore, the mean annual precipitation can be used as an index for the 24-hourstorm depth. The results presented in Chapter 3 have shown that the mean annualprecipitation in coastal British Columbia increases up to 400-800 m in elevation, and thenlevels off or even decreases at the upper elevations. Also, the study of the storm precipitation118ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIApresented in Chapter 4 and the analysis of extreme historical storms that will be presented inthe next Chapter indicate that the storm distribution across a watershed follows a similarcurvilinear pattern, increasing at the lower elevations and then leveling off or even decreasingat elevations above 800 m. Similar results for the annual and seasonal precipitation have beenfound for the northern Cascade region in Washington State which indicates that the results ofthis study may be transferable to the greater region of the Pacific Northwest.The above conclusions together with the results of the analysis of the storm timedistribution are significant for the estimation of the peak flows from the coastal watersheds.Furthermore, the result of this study indicates that the extreme 24-hour rainfall can beestimated as a certain percentage of the mean annual precipitation. Of the 269 precipitationstations in coastal British Columbia, 96 are recording gauges, and from these stations only 61have records long enough to assure a reliable frequency analysis. Therefore, this studyindicates that, until more extensive and long-term recording rain gage data are available, theannual data are a valuable guide for design flood estimation.119ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA0z00U0UICDzUIC.)UI00z0Cl)U0UICDzUIC)UI010090807060504030201001009080706050403020100YEARS OF RECORD0 200 400 600 800 1000 1200STATION ELEVATION (m)Fig. 5.1. Distribution of the coastal British Columbia stations witha) years of record, and b) station elevation.12026 24 \u201812 20 18 16() z LU 0 LUI\u20190LL10 6 4 2-0JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECMONTHFig.5.2.Monthlydistributionoftheoccurenceoftheannualmaximum24-hourstormsatVancouverHarbour(53years).ZTCUMULATIVEPERCENTAGEOFSTORMPRECIPITATION-rs3C.010)\u2022%J0)CD000000000091:13CD30\u20220\u20220.00CCl)00)VI914fl7O3HSI1I197VISVO31OIkVIOJSNDIScIII1OH-frZ\u00e7Jdvy3ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA10090807060504030z200Io9O8070Cl) 6050LuCD30zLu0LuQ 0Lu>I-. 1009080o 706050403020100Fig.5.4. Comparison of the time probability distributions for various stationsa) ten percent curves, b)fifty percent curves, and c) ninety percent curvesTIME (h)123-nCDC;\u2019CDp.j7fCUMULATIVEPERCENTAGEOFSTORMPRECIPITATION-\u2018rzc..n-Co0000000000-Im0C0)0VIffWfl7O3HSIJJJ7VISVO3fOd1VIOISNDISIIflOH-frZ\u00e7J?th2l(3100z 0901-8000Ui 70a: 605040\u2014a: 30200tO 0TIME(hours)Fig.5.6Comparisonof theaveragestormtimedistributionintheSeymourRiverwatershedwiththeresuftsoftheMelone(1986)analysisforcoastalBritishColumbia.04812162024z100 90 80 70 60 50 40 30 20 10 0TIME(hours)Fig.5.7ComparisonoftheaveragestormtimedistributionsintheSeymourRiverwatershedwiththeresultsoftheHogg(1980)analysisforcoastalBritishColumbia.04812162024ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIA1009080706050403020z0IC.)90080600U-o 40w3020UiC-)crUi0Ui> 1009080D706050403020100Fig. 5.8. Comparison of the time probability distributions of the Seymour Riverwatershed and three coastal British Columbia stations, a) ten percent curves,b) fifty percent curves, and c) ninety percent curves.TIME (hours)127100z Q90I-80o0w7060LI.o w50w40000W 30w20010 0TIME(hours)Fig.5.9Compansonof syntheticstormswiththeaveragetimeprobabilitydistributionsintheSeymourRiverwatershed.04812162024340320300280260240E220-J 200Z180160140120100 80 60ELEVA11ON(m)Fig.5.10. Distributionof rainfall withelevationforextreme24-hour stormsof variousreturnperiodsintheSeymourRiverwatershed.0200400600800R24=0.057a260240220200p180-J-J140120;100 80 60 40 20a.fl2=0.815SEE=19.4mma I\u2022UU.a\u2022.U_a\u2022.\u2014a a..U.\u2022\u202210-YEAR24-HOURRAINFAIi____REGRESSIONUNEIIIIIIIIII60010001400180022002600300034003800MEANANNUALPRECIPITA11ON(mm)Fig.5.11.Relationshipofthe10-year24-hourrainfallandmeanannualprecipitationforthesixty-onerecordingstationsincoastalBritishColumbia.ChapterS. 24-HOUR DESIGN STORM FOR COASTAL BRITISH COLUMBIARAINFALLPt\u2018sfFig. 5.12. The watershed model flow chart.4INFILTRATIONCONTROLLEDP= P-Pft S*JKF,1j KFJ KSFAST RUNOFF SLOW RUNOFFWATERSHED OUTFLOW13118 17 16 15 14 13 12 11 10 9 8 7TIME(hours)Fig.5.13Comparisonofthe10-year observedfloodhydrographwithsimulatedhydrographsusingsyntheticandderivedhyetographs,for theJamiesonCreekwatershed.0204060CHAPTER 6STUDY OF HISTORICAL LARGE STORMS6.1 IntroductionIn Chapters 4 and 5 the spatial and temporal distribution of the storm precipitation and the 24-hour maximum rainfall was analyzed. The storms analyzed were the largest for the record ofthe stations in the Seymour River watershed. However, it is important to examine historicallarge storms that have caused flooding in the greater area of Vancouver and the lower Fraservalley. In this Chapter the synoptic meteorological conditions of these severe storms will bepresented, their spatial and time distribution will be examined with the available data from thegreater Vancouver area and Seymour River watershed, and finally they will be compared withthe results of the study of storm precipitation in Chapter 4 and the analysis of the 24-hourrainfall presented in Chapter 5.Five storm periods that have caused severe flooding will be presented. Summaries ofthe synoptic conditions of these storms are available from Atmospheric Environment Servicestorm studies. The storms that will be presented occurred in winter, fall and summer so thattheir analysis will give an overall description of the meteorological conditions of floodproducing mechanisms.[\u201833Chapter 6. STUDY OF HISTORICAL LARGE STORMS6.2 The July 11-12, 1972 Rainstorm6.2.1 Synoptic conditionsThe first storm occurred in July. July is one of the driest months of the year for the studyarea. But on July 11-12, 1972 the area was deluged by a rainfall unprecedented for thatmonth. This storm was frontal and occurred under a strong southwesterly flow of warm moistair, not common for the dry season of April to September, and it had many characteristics ofthe winter storms.On July 11 at approximately 10 a.m. Pacific Standard Time the leading frontal waveapproached Vancouver bringing continuous rainfall. Twenty four hours later the cold front ofthe second wave passed over the area. Its passage through the lower Fraser Valley abruptlycut off the continuous rainfall, although a few post-frontal showers where observed, especiallyin the northeast Lower Fraser Valley. Radiosonde soundings from Quillayute, Washingtonhelp to explain the development of the strong moist southwesterly upper- air flow over thecoast (Schaefer, 1973). On July 11, 1972, an air mass with wet bulb potential temperature of12- 14\u00b0C extended up to 500 mb and the layer between 600 and 750 mb was very dry. Above500 mb the air was near saturation at a wet bulb potential temperature of 18-19\u00b0C and warmadvection was underway at all levels. By the morning of July 12 the entire air column hadwarmed by 4-5\u00b0C except for the layer just above the cooler surface water. The wet bulbpotential temperature ranged from 14\u00b0C at surface, to 17\u00b0C at 600 mb and 19-20\u00b0C at 500mb. At 500 mb strong west-southwest winds of 60 knots were recorded (Schaefer, 1973).Twelve hours later the winds had increased to 70 knots, the frontal system had passed over theFraser valley and cold winds swept the area lowering the mean daily temperature by 2\u00b0C.134Chapter 6. STUDY OF HISTORICAL LARGE STORMS6.2.2 Spatial distributionSchaefer (1973) compared data from 78 A.E.S. stations. He found that the total stormprecipitation increased from 60-77 mm at low elevations to about 150 to 180 mm at themountains. Schaefer\u2019s data for the high elevations were based only on measurements on theNorth Shore mountain slope stations. Such a station, the Hollyburn Ridge at 910 m recordedan all time record of 167.4 mm. Schaeffer presented no data for the valleys of the NorthShore which extended further into the mountain range. Examination of the Seymour valleydata shows that the total storm depth was 128.4 for station lOA, 142.2 mm for station 14A,143.3 mm for station 21A and 173.5 mm for station 28A. These data compared to 148.8 mmat Seymour Falls Dam and 89.7 mm at Vancouver Harbour give a ratio to base precipitation ofabout 1.6 for the upper and mid-watershed except for the station 28A for which the ratio is1.93. These values are well below the values found in Chapter 4 which are in the order of 2.8-3.0.The 24-hour precipitation was not larger than the annual 24-hour maximum for mostof the stations in the area. However, the Hollyburn Ridge station, which received the greateststorm accumulation reported by all stations in the area, showed a new record for the dailyprecipitation of 167 mm. For the upper Seymour watershed the 24-hour precipitation had areturn period less than 2 years.However, the picture changes when these accumulations are compared with the Julyrecords. The daily accumulation of 73.7 mm in Vancouver Harbour is the largest for 53 yearsof record. For Seymour Falls Dam the 103.1 mm is the third largest daily rainfall in 63 years.So, in general this storm was not extreme by annual standards, but by July standards wasexceptional.135Chapter 6. STUDY OF HISTORICAL LARGE STORMSThe duration of the continuous storm increased from 25 hours at low lands to 30-3 1hours on the mountain tops (Schaefer, 1973). For the Seymour watershed stations theduration ranged from 46 hours at 1OA and 21A to about 60 hours at 28A. Comparing thesevalues with the 29 hours of storm duration at Vancouver Harbour shows a duration ratio ofabout 1.55 for 1OA and 21A and 2.07 for 28A. Schaeffer (1973) pointed out that the meanprecipitation rate increased with elevation from 2.5 mm\/h at low elevations to 8 mm\/h at themountain tops. However, from the above results we can see that the average precipitationintensity for the upper Seymour remained relatively constant at about 3 mm\/h, as much as thelow elevation value.As the Schaefer study (1973) showed, the large precipitation on the mountain slopeswas primarily due to greater intensities. However, the results of this study suggest that in theSeymour valley which extends further into the mountainous range, the larger precipitation wastotally due to larger duration. As Schaefer (1973) pointed out, the moist but stable air massforced to rise over the mountain slopes released its moisture at much higher rate than at lowelevations. However, the valley convergence was not as efficient as the orographic lifting.But the storm duration was more prolonged at the upper Seymour River watershed and theincreased roughness of the mountain range is probably the reason for this.6.2.3 Time distributionFigure 6.1 compares the time distribution of the storm with the time probability distributioncurves found in Chapter 4. The time distribution of the storm in station lOA is well belowthe 50 % probability curve whereas the distribution for l4A is below the 50 % curve for the40 % of the storm duration and then it rises over this curve. These results show that the stormintensity remains relatively constant for most of the duration for 1OA whereas the higher136Chapter 6. STUDY OF HISTORICAL LARGE STORMSintensities occur at the beginning of storm at 14A. However, both curves can beapproximated by a line.6.3 The December 13-18, 1979 Rainstorms6.3.1 Synoptic conditionsDuring most of December 1979 much of British Columbia was affected by a mild moistsouthwesterly flow of air both at the surface and aloft. Throughout the early days ofDecember 1979 usual amounts of precipitation had fallen over the southern coast. OnDecember 13, 1979 the wet southwesterly moisture condition was intensified by the pressureof an almost stationary frontal system which stayed over the lower Fraser valley and southernVancouver Island. A series of minor depressions and waves which moved constantlynortheastward along the front delayed the passage of the front over the area. As a result,southern coastal British Columbia received large amounts of continuous rain, very mildtemperatures and strong winds. The rain was intensified as the waves approached the coast(Chilton, 1980). Inspection of the December 13 tephigraph from Quillayute, Washingtonreveals a stable profile with a warm and moist air mass aloft. The wet bulb potentialtemperature was 12\u00b0C at 500 mb, and the persisting surface dew points reached 10\u00b0C for thedays of December 13 and 14 (Schaefer, 1980). This situation described the first storm period.The heavy rain during this period resulted in mudslides and local flooding causing widespreadinconvenience.After the heavy rain of December 14, the front and the associated southwesterly flowof moist air were moved to the south. In part this was in response to the strong continentalArctic high pressures centered over the interior of British Columbia. The mean daily137Chapter 6. STUDY OF HISTORICAL LARGE STORMStemperature at Vancouver Harbour dropped from 6.5\u00b0C to 2.6\u00b0C on December 15, 1979.Radiosonde sounding revealed mid-level drying and surface cooling (Schaefer, 1980). Thesouthwesterly flow during this time affected southern Washington and Oregon states.However, on December 16 and 17 the southwesterly flow again moved north andstarted affecting the southwestern Coastal British Columbia. The tephigraph of December 16showed a very stable and moist profile with wet bulb potential temperatures reached 16\u00b0C forthe 600 mb level and above. During this period, the persisting dew points reached 11\u00b0C. OnDecember 18 the profile became drier aloft and less stable producing showers that followedthe passage of the frontal system over the area.The synoptic conditions that produced these two storms do not occur every winter onthe south coast at least not to the extent of the December 1979 events (Chilton, 1980).However, the resulting storms were very similar to most of the winter storms which occur inthe area.The two distinctive storm periods of December 13-14 and December 16-18 will beanalyzed separately. Even though these two storms were generated by the same system theirdistribution was different.6.3.2 The December 13-14, 1979 storm6.3.2.1 Spatial distributionSchaefer (1980) compiled data from 18 stations in the greater Vancouver area and VancouverIsland. For the first storm larger precipitation fell over Vancouver Island than in theVancouver area. In the Seymour River watershed the precipitation gradient was lower thanthe average gradient reported in Chapter 4. The storm precipitation increased from 112.3 mm138Chapter 6. STUDY OF HISTORICAL LARGE STORMSat Vancouver Harbour to 196.2 mm at Seymour Falls Dam and then decreased to 152.9 mm at1OA and leveled off at 173.2 mm at 14A. These values represent a ratio of 1.75 at SeymourFalls Dam, 1.36 at 1OA and 1.54 at 14A. The maximum 24-hour rainfall fallen at VancouverHarbour (80.5 mm) during the storm had a return period of about 4 years. The return periodsdecreased at the mountains. The 121.4 mm in 24 hours at 1OA had 2.5 years return periodwhereas the 123.2 mm at 14A was only a 2-year storm. Examination of the data from otherstations in the area showed that this pattern of the decrease of return periods with elevationwas more general over the southwestern British Columbia (Schaefer, 1980). However,because of the wet period preceding the storm and the heavy 24-hour rainfalls exceeding 120mm, the chance for landslides had increased. Actually, many landslides were reported duringthe two-day period between December 13 and 14.Examination of the duration of the storm showed that the storm had about the sameduration at the lower elevations as in the mountainous area, being 41 hours at VancouverHarbour and only 46 hours at 14A. As a result the larger rainfall at the upper Seymourwatershed was the result of higher intensities. The average rainfall intensity increased from2.73 mm\/h at Vancouver Harbour to about 3.75 mm\/h at lOA and l4A. These valuesrepresent an increase equal to 37%. The same increase has also been observed for the stormprecipitation.The maximum hourly intensity increased by 70% between the lower and upperwatershed. Checking the start time of the storm reveals that the storm started 3 hours earlierat the upper watershed. This resulted from the increased roughness of the mountains and theorographic lifting which promote the condensation of the hydrometeors.139Chapter 6. STUDY OF HISTORICAL LARGE STORMS6.3.2.2 Time distributionFigure 6.2 compares the time distribution of the December 13-14 storm with the curvesdeveloped from the analysis of the 175 storms presented in Chapter 4. It seems that thedistribution of the storm in time changes as the elevation changes (Fig. 6.2). At VancouverHarbour the largest intensities occurred at the beginning of the storm while the heaviestintensities in the Seymour River watershed occurred at the end of the storm (Fig. 6.2).6.3.3 The December 16-18, 1979 storm6.3.3.1 Spatial distributionThe second storm of the December 13-18, 1979 period was more severe for the lower Fraservalley than for Vancouver Island. This is the result of the passage of the front to the northeastand its intensification, as the radiosonde soundings have shown (Schaefer, 1980). This secondstorm precipitated larger amounts on the mountains and valleys than at the low lands. Thetotal storm depth at Vancouver Harbour was 155.8 mm while 347.4 mm fell at Seymour FallsDam, 249.7 mm at 1OA and 243.1 mm at 14A. These values represent an increase of about220% at Seymour Falls Dam and about 60% for the upper watershed over the precipitationaccumulation at the Vancouver Harbour station.However the intensification of the rain was greater at the lower elevations. The 24-hour maximum rainfall of 121.1 mm at Vancouver Harbour had a return period of 60 years.The return periods decreased to about 3 years at Seymour Falls Dam (151.8 mm), 4 years at1OA (139.7 mm) and 2 years at 14A (124.5 mm). The 24-hour rainfall accumulations, as theabove data show, increased by 25% at the middle of the Seymour valley and leveled off at the140Chapter 6. STUDY OF HISTORICAL LARGE STORMSupper watershed.Similar variation of the return periods has also been observed in the greater area ofVancouver. For example, the daily precipitation of 87.5 mm at the Vancouver InternationalAirport had a return period of 70 years while the 136 mm fallen at Coquitlam Lake stationwere only a 3-year 24-hour storm accumulation.Examination of the storm duration reveals that the storm lasted 56 hours at VancouverHarbour and 67 hours at 1OA and 14A. These values represent an increase of 20% in durationbetween the upper and the lower watershed. This means that the larger precipitation at theupper watershed was not only due to the longer storm duration but also due to greater stormintensities. Comparison of the average storm intensities showed that the Vancouver Harbourvalue of 2.78 mm\/h increased by about 30% at the upper watershed. However, the maximumhourly intensity remains relatively constant. In light of these results one can say that, at leastfor the upper Seymour River watershed, the larger accumulations were due to both longerduration and higher storm intensities.Examination of the time that the storm started shows that the storm started first at theupper and mid-watershed, namely about 5 hours earlier at 1OA and 2 hours earlier at 14A thanat Vancouver Harbour.6.3.3.2 Time distributionFigure 6.3 shows the time distribution of storm for the stations Vancouver Harbour, 1OA and14A. The storm has different distribution at Vancouver Harbour than it has at the upperwatershed. At Vancouver Harbour the highest intensities occurred in the mid-duration of thestorm whereas for the two other stations there is intensification of the storm just before its halfduration, followed by a lull.141Chapter 6. STUDY OF HISTORICAL LARGE STORMS6.4 The October 25-31, 1981 Rainstorms6.4.1 Synoptic conditionsThe weather of October 1981 over the southwestern British Columbia was dominated by threelarge scale circulation patterns (Schaefer, 1982). These included a low pressure trough(October 1-9), a high pressure ridge (October 10-24), and a southwesterly flow (October 25-31). Frontal storms generated in the North Pacific Ocean and transported by the westerliesimpinge on the southwestern coast of British Columbia on October 25. For the first days ofthe period of October 25-31, the weather systems were similar to the winter systems thatproduce large precipitation in the area. The wet weather during October 25-27 wasaccompanied by freezing levels well above 2000 m and at about 1500 m on October 28.However, by that time satellite images showed that tropical moisture generated in the vicinityof Hawaii was swept into the southwesterly flow, ahead of an advancing frontal zone (Horita,1981). From Quillayute tephigrams, it appears that this injection of tropical moisture in thesystem created a large scale instability (Horita, 1981). This warm moist air associated withthe final heavy rainstorm during October 30-31 brought freezing levels up to 3000 m onOctober 31.Two storms can be identified for the period October 25-31, 1981. The first is thestorm of October 25 to 28 and the second started on October 28 and lasted until November 1.These two storms were associated with two disastrous events. During the first storm, alandslide occurred just before midnight of October 27 in the small M Creek watershed whichflows from the steep mountain slopes into Howe sound (Fig. 2.3). The mixture of mud, treetrunks, water and boulders burst down on the highway, washing out the bridge and killing fivepeople in a car that was on the bridge. Two more cars drove over the edge of the 15 meter142Chapter 6. STUDY OF HISTORICAL LARGE STORMSdrop where the bridge used to be. Also, a small house built near the beach was washed out tosea by the debris torrent while another house was battered by the debris.The second storm was very severe for the area of North and West Vancouver. Theheavy rains on October 30 and 31 resulted in flooding of Seymour River, Lynn Creek andMosquito Creek in the North Shore mountains. The floods claimed the life of a man sweptaway by the floodwaters of Mosquito Creek. The roaring waters of the rivers and creeks ofthe area burst and overtopped their banks flooding residential areas causing millions of dollarsin damage. The muddy flood waters of Seymour River eroded the footings of two bridgesjeopardizing the stability of the structures. Finally, a mudslide into Capilano Lake reservoircreated turbitity problems in the drinking water.The spatial and temporal distribution of the two storms will be examined separately inthe next paragraphs.6.4.2 The October 25-28, 1981 storm6.4.2.1 Spatial distributionThe first storm of the period October 25-28 was very severe in the Vancouver area. Thestorm was intensified by the convergence of the valleys but not by the orographic lifting, asthe data show. For example the Hollyburn Ridge station (910 m) on the southern slopes ofHollyburn mountain received only 83.8 mm while the Seymour Falls Dam station in thenearby Seymour River valley, received 158.9 mm in the same period. In the Seymour Riverwatershed the storm exhibited very large increases. The Vancouver Harbour stormprecipitation was only 37.6 mm whereas the rainfall increased to 158.9 mm at Seymour FallsDam, 223 mm at 1OA, 223 at l4A and 203.5 at 25B. These values show that the storm143Chapter 6. STUDY OF HISTORICAL LARGE STORMSrainfall increased by about 4 times between the lower and mid-watershed and then increasedand fmally leveled off in the upper watershed at a ratio of about 5.5.The storm was not as large or as severe as the storm depth ratios might indicate. The24-hour rainfall at Vancouver Harbour had a return period of less than 2 years. But theseverity increased to 3.5 years at lOA (134 mm), to 4 years at 14A (149.8) and 2.5 years at25B (142.2 mm). Station 25B is located 15 km east of M Creek watershed and the 24-hourrainfall accumulation was 142 mm. O\u2019Loughlin (1972) showed that when the 24-hour rainfallexceeds the 120 mm the steep slopes of the area are prone to landslides, and therefore thisstorm rainfall accumulation is capable of producing landslides in the already wet steephillslopes.The storm lasted only 75% longer at the upper watershed. As a result, the large rainfallat the top of the watershed was due to large average storm intensities, which increased byabout 200% at the upper Seymour River watershed.The storm started about 6 hours earlier at the upper Seymour River watershed than atVancouver Harbour.6.4.2.2 Time distributionFigure 6.4 shows the time distribution of the storm for four stations. It seems that the timedistribution of the storm did not change with elevation. High intensities occurred for allelevations at the middle of the storm duration and then for the rest of the storm the stormintensities were moderate.144Chapter 6. STUDY OF HISTORICAL LARGE STORMS6.4.3 The October 28-31, 1981 storm6.4.3.1 Spatial distributionThe second storm of the period October 25-31, 1981 was more severe than the first storm.The injection of tropical highly unstable moist air in the frontal system is the causative reasonfor the severity of the storm. For this storm of October 28-31, as for the October 25-28 storm,the valley convergence was much more efficient than the orographic lifting. Hollyburn Ridgereceived only 212 mm while Seymour Falls Dam received 486 mm. In the upper SeymourRiver watershed the storm precipitation dropped to 213.4 mm at bA, 243.9 mm at 14A and278.6 mm at 25B. These precipitation accumulations represent a ratio to the VancouverHarbour precipitation of 4.54 for Seymour Falls Dam, 2.0 for bA, 2.3 for 14A and 2.6 for25B. Also, it is significant that the precipitation accumulation at Hollyburn Ridge station issimilar to the accumulations observed at the Seymour valley stations located further inwardsin the mountainous area.The severity of the storm increased with elevation in the Seymour River watershed.The return period of the 78.5 mm in 24 hours at Vancouver Harbour had a return period of 3.5years while the return period increased to 9 years at Seymour Falls Dam (205.5 mm), 10 yearsat 14A (183.9 mm) and 15 years at 25B (210.9). The return periods of the 24-hour storm inthe valleys on the North Shore mountains were larger than on the mountain slopes. Forcomparison the return period at Hollyburn Ridge was only 4.3 years (136.4 mm) while atLynn Creek it was 10 years (136 mm).These extreme storms were dependent on both the higher intensities and the prolongedduration. The duration increased by 50% in the mountains whereas the average storm rate andthe maximum hourly intensity increased by 70%. Furthermore, the storm started, on average,145Chapter 6. STUDY OF HISTORICAL LARGE STORMS5 hours earlier in the upper Seymour watershed than at the Vancouver Harbour station.6.4.3.2 Time distributionFigure 6.5 shows that the storm has similar time distribution pattern at all elevations. Thelarger intensities were observed at the middle of the storm at about October 29.6.5 The November 8-11, 1990 Rainstorm6.5.1 Synoptic conditionsNovember is the wettest month of the year for the southwestern British Columbia. On theevening of November 8, 1990 a strong warm front associated with a large Pacific low pressuresystem moved into southwestern British Columbia. Satellite images revealed that this warmunstable air mass was generated in the North Pacific close to Hawaii. Highly moist tropicalair was injected in the system. The storm was named \u201c Pineapple Express\u201d by the U.S.National Weather Service forecasters.The storm moved slowly as a number of waves and troughs progressed eastward,causing heavy precipitation. The heavy precipitation started in the early morning hours ofNovember 9th and lasted until the evening of November 10th in the lowland areas andcontinued until the evening of November 11th in the mountainous areas. The storm coverageextended from northern coastal British Columbia to northern Washington State.Severe widespread flooding was reported in all the coastal region, but the hardest hitareas were the eastern Lower Mainland and the Whatcom County in Washington State. Thestrong southwesterly flow of air was associated with high freezing levels and so the flooding146Chapter 6. STUDY OF HISTORICAL LARGE STORMSwas aggravated by the melting of the accumulated snow. In Chilliwack and Sumas Prairielarge areas were evacuated when the Chilliwack River rose 2 to 2.5 m, overtopping its banksand flooding large areas. The flooding was estimated to have a return period of 200 years.Poor highway conditions were blamed in the deaths of two people and injuries to four others.The estimated damage approached 10 million dollars. Furthermore, clogging of the drainagesystems resulted in flooding of basements and streets in Vancouver. The heavy precipitationresulted in numerous mudslides and rockslides which disrupted traffic on the major highways.In Washington State the damage was comparable to that of the Chilliwack - Sumas areas.6.5.2 Spatial DistributionThe precipitation amounts varied greatly across the south coast of British Columbia. Thesouthwestern and central Vancouver Island reported amounts of 150 to 200 mm but areas inthe Northern Vancouver Island received even larger amounts. In the greater Vancouver areathe rain amounts varied around 100 mm, whereas they increased in the north and east lowerFraser Valley due to orographic lifting and valley convergence. For example, in Squamish,north of Vancouver the rainfall accumulation was 310 mm while in the eastern lower Fraservalley the precipitation amounts rose quickly from over 150 mm at Abbotsford to 200 mm atChilliwack and to 337 mm at Hope.In the Seymour River watershed the same pattern was observed. The precipitationincreased from 108 mm at Vancouver Harbour to 460 mm at Seymour Falls Dam and thenleveled off at 339.6 and 354.3 mm at stations 14A and 25B, respectively. These values showthat the precipitation increased by more than 4 times between the lower and upper watershedand then decreased and leveled off at a ratio of about 3.2.The severity of the storm increased in the same pattern as the storm depths. The return147Chapter 6. STUDY OF HISTORICAL LARGE STORMSperiod of the 24-hour rainfall increased from about 1 year at Vancouver to 9 years atAbbotsford (79.6 mm), 10 years at Chilliwack (99.4 mm) and to more than 100 years at Hope(173.1 mm).In the Seymour River watershed the severity of the storm increased up to the middleposition and then declined. The return period of the 24-hour rainfall at Vancouver Harbourwas 1.2 years (55 mm) and increased to 33 years at Seymour Falls Dam (300 mm) and thendecreased to 10-years and 5-years at 14A (173.3 mm) and 25B (170.2 mm), respectively. The300 mm reported in Seymour Falls Dam is the second largest 24-hour event of record, and thelargest value reported during this storm for the whole southwestern British Columbia. Theresult of the very large precipitation accumulations were that many landslides, mudslides androckslides occurred in the mountainous area east of Hope.Examination of the storm duration showed that the storm lasted about 57 hours inVancouver Harbour and about 80 hours at the upper Seymour River watershed. These valuesindicated a ratio of storm duration of about 1.40 between the low level areas and the upperstudy watershed.The above results reveal that the larger storm precipitation at upper and mid-watershedwas mainly due to the larger storm intensities, which increased from 1.89 mm\/h at VancouverHarbour to 4.26 mmlh and 4.51 mm\/h at 14A and 25B, respectively. These values show anincrease of about 2.3 times between the upper and the lower watershed. Furthermore, thesame has been observed for the maximum hourly intensity which increased from 3.8 mm\/h atVancouver Harbour to about 12.0 mm\/h at the upper watershed, representing an increase ofmore than 300%.The storm started, on average, about 24 hours earlier at the middle and upperwatershed than at Vancouver Harbour. These results along with the findings for the stormduration show that the orographic lifting and the valley convergence triggered the intense148Chapter 6. STUDY OF HISTORICAL LARGE STORMSstorm earlier and the mountain roughness delayed the passage of the storm by about 24 hours.6.5.3 Time distributionThe time distribution at the Vancouver Harbour was uniform and followed the 50% timeprobability curve (Fig. 6.6a). At the upper watershed the high intensities were observed afterthe middle of the storm. This pattern remained consistent for the two upper elevation stations14A and 25B (Fig. 6.6b and c).6.6 The November 21-24, 1990 Rainstorm6.6.1 Synoptic conditionsFor the second time in November 1990 a strong frontal storm impinged on southwesternBritish Columbia bringing heavy rain. This system, like the previous one, had been injectedwith moist subtropical air during its passage over the North Pacific. However, this system didnot stay over the area as long as the November 8-11, 1990 system. It moved from asouthwesterly direction to the northeast. Heavy rain did fall but was not as continuous or asheavy as the previous storm. Flooding was reported in greater Vancouver and Victoria but theworst flooding occurred in Washington State, north and west of Seattle.6.6.2 Spatial distributionThis storm was not as severe as the previous November 8-11, 1990 storm. The storm wasintensified over the mountain slopes and the valleys as compared to low level areas. The total149Chapter 6. STUDY OF HISTORICAL LARGE STORMSstorm depth increased from about 60 mm in Vancouver to 101.3 mm at Abbotsford, 132.1 mmat Chilliwack and 247.8 mm at Hope. The rain shadow effect was not as strong with thissystem so that the heavy precipitation extended further east of the Coast Mountains. Largeamounts of precipitation in the Victoria area caused flooding (94.8 mm in Victoria Airport).In the Seymour River watershed, large increases in the valley had occurred. The totalstorm depth at Vancouver Harbour was 57.4 mm but increased to 439.5 mm at Seymour FallsDam. At the upper watershed the storm precipitation amount decreased and leveled off at 240mm. These values represent an increase of about 700% at Seymour Falls Dam and 300% atthe upper watershed over the precipitation at Vancouver Harbour.The intensification of the rain followed a similar pattern to that of the rain distribution.The 24-hour maximum rain at Vancouver Harbour (27.4 mm) had a return period smaller thana year, but increased at Seymour Falls Dam (228.3 mm) to 10.5 years and 4 years at 1OA(139.4 mm) and 8 years at 14A (178.1 mm). The same distribution of the return periods hasbeen observed for the greater area. For example the return period of the daily rainfall atVancouver Airport, Abbotsford and Chilliwack was less than one year while it was 15 years atHope.The storm duration increased from 37 hours at Vancouver Harbour to 61 and 71 hoursat 1OA and 14A, respectively. The above results of duration and precipitation indicate that thehigher intensities at the upper watershed are mainly responsible for the increase ofprecipitation at the higher elevations as compared to the middle and low elevationsExamination of the average storm intensity revealed that the storm intensity increased by130% between the lower and upper watershed (from 1.55 mm\/h to about 3.5 mm\/h). Also,the maximum hourly intensity increased by about 170% between the lower and upperwatershed.The storm started about 20 hours earlier at the upper Seymour River watershed than at150Chapter 6. STUDY OF HISTORICAL LARGE STORMSVancouver Harbour.6.6.3 Time distributionThe storm had different time distribution at Vancouver Harbour station from that at the upperstudy watershed. At the Vancouver Harbour station the storm was not continuous and twodifferent storm periods can be distinguished (Fig. 6.7a). This resulted in two steep parts ofthe curve in the beginning and the end and a flat area at the middle of the storm duration. Atthe upper watershed the time distribution is similar at 1OA and 14A stations. The largerincrease after the middle duration represents the heavy rainfall on November 23, 1990. Onecan observe that this part of the time distribution curve is reasonably linear (Fig. 6.7b, c).6.7 SummaryExamination of five storms showed that the main flood-producing mechanism in the area isthe frontal systems that developed over the North Pacific Ocean and moved eastward untilthey impinge on the coastal British Columbia. The warm, wet flow of air is occasionallyintensified with the injection of humid tropical air, and, as a result, very large precipitationaccumulations are generated in the study area.These frontal systems are capable of producing more than one storm as themeteorological conditions change and the systems move away north or south and then againmove over the study area. These systems occur mainly during the winter and fall months andrarely during summer. The importance of this for the hydrology of the region is that the soilmoisture storage is mostly filled during the wet winter and fall months and combined with theheavy precipitation results in large floods and slope instability.151Chapter 6. STUDY OF HISTORICAL LARGE STORMSFrom the examination of the limited data in the Seymour River watershed, it isevident that, in general, the severe storms have a spatial distribution similar to the distributionfound in the analysis of the 175 storms in Chapter 4 and the results of the 24-hour maximumrainfall in Chapter 5. Furthermore, the increased roughness and the orographic lifting and thevalley convergence cause the severe storms to start earlier at the upper Seymour Riverwatershed than at the low-level Vancouver area. Also, for the same reasons the duration ofthe storms is significantly increased in the mountainous area. As a result, the largeraccumulations at the upper study watershed are due to the high intensities and the longdurations. The time distribution of the storms analyzed varied for the same station from stormto storm but it was within the range of the results presented in Chapter 4.The comparison of the severe storms with the findings of the analysis of the stormprecipitation in the Seymour River watershed shows that, in general, the severe storms havesimilar spatial and temporal distribution characteristics to the less severe storms analyzed inChapter 4 of the thesis. This is significant because it confirms that the previous results can beused for the estimation of extreme storms that produce flooding and other related problems inthe area. Furthermore, similar storm studies by Atmospheric Environment Service (Schaefer,1979a; Schaefer, 1979b) indicate that flood producing storms in other areas of coastal BritishColumbia have similar characteristics as those observed in the greater Vancouver area. This isnot surprising since the same large-scale atmospheric circulation produces storms that impingein the north or south coast of British Columbia depending of the regional circulation patterns.152z0aC.)w0LL0wC-)w0wCUMULATIVE PERCENT OF STORM DURATIONFig. 6.1 Comparison of the time distribution of the July 11 -12, 1979 storm withtime probability distribution curves at (a) station I OA and (b) station I 4A153Chapter 6. STUDY OF HISTORICAL LARGE STORMS100..\u2014 (a)\u2014\u2018....... ,I.:\u2018, ..,0 \/ \/\u2022.-\/, .. , ,,60.\u2022. \/ ,\/\/ 50%0 \/ i\/ \/ ---- 10%n. \u2018I \/ \/.\/\/#0 \/ \/\/ \u2014-\u2014 90%I .\u2022\u2022 \/Afl \/ ,.\/\u2022 \/ 30%-T... f ,\/ \/, ... I \/ \/ \u2014\u2014 70%I, .\u2022\u2022 \/ \/\/ \/\u2022\u2018v. ... f \/ \/\u2018..\u2022\u2022 \/ ,. \/ \u2014..\u2014.. JULY 11-12, 1972g:\/,\/ \/v ,...\/ \/ \/I\/I ,\u2022\u2022 \/10 \/\u2018\/... \/ \/4\u2019 .0 \u2014I F..\u2022 .\u2014\u2018 I I I I I I I I0 20 40 60 80 1001009080706050403020100 0 20 40 60 80 100Chapter 6. STUDY OF HISTORICAL LARGE STORMSz000waU0I\u2014zUi0UiaLU50%10%90%30%70%DECEMBER 12-14,19791009080706050403020100 0 20 40 60 80 100CUMULA11VE PERCENT OF STORM DURA11ONFig. 6.2 Comparison of the time distribution of the December 12-14, 1979 stormwith time probability distribution curves at (a) station Vancouver Harbour(b) station 1OA and (c) station 14A154Chapter 6. STUDY OF HISTORICAL LARGE STORMSz000wCCl)U0Izw0UiCUi50%10%90%30%70%DECEMBER 16-19,1979100 \u202290 \/4-\/\/\/ a80 \/ ...:\u2018\/\/\/..70 -6050...... \/\/\/ \/\u201c Is ...\/ \/\u2018,uI.. F \u20225.\u2022 \/,V \u2018.:\u2022\/ ,20 \/\u2022Z\u2019 ,:\u2018\u2018100 \u20140 20 40 60 80 10010090807060504030201000 20 40 60 80 100CUMULATIVE PERCENT OF STORM PRECIPITATIONFig. 6.3 Comparison of the time distribution of the December 16-19, 1979 stormwith time probability distribution curves at (a) station Vancouver Harbour(b) station I OA and (C) station 1 4A155Chapter 6. STUDY OF HISTORICAL LARGE STORMS50%30%\u2014\u2014\u202270%OCTOBER 25-28 1981CUMULATIVE PERCENT OF STORM DURATIONFig. 6.4. Comparison of the time distribution of the October 25-28, 1981 stormwith time distribution probability curves at (a) station Vancouver Harbour(b) station 1 OA, (C) station 1 4A and (d) station 25B1106806040200-100 -80604020z0pciwaCIzUiC.)UiaUi( I 20 40 60 80 100U 0 20 40 60 80 100156chapter 6. STUDY OF HISTORICAL LARGE STORMS1806040200 20 40 60 80 10050%30%\u201470%OCTOBER2e-31,1981CUMULA]1VE PERCENT OF STORM DURA11ONFig. 6.5. Comparison of the time distribution of the October 28-31, 1981 stormwith time distribution probability curves at (a) station Vancouver Harbour(b) station 1 OA, (C) station 1 4A arid (d) station 25BI VU806040200 0 20 40 60 80 100100157Chapter 6. STUDY OF HISTORICAL LARGE STORMSz000w0Cl)LI0Izw0waUi10090807060504030201001009080706050403020100\u2014 50%10%90%30%\u2014\u2014 70%NOVEMBER8-12,1990Fig. 6.6 Comparison of the time distribution of the November 8-11, 1990 stormwith time probability distribution curves at (a) Vancouver Harbour(b) station 1 4A and (C) station 25B0 20 40 60 80 100CUMULATIVE PERCENT OF STORM DURATION158Chapter 6. STUDY OF HISTORICAL LARGE STORMSz0I.0C-)LU0\u201450%10%90%30%\u2014\u2014 70%NOVEMBER 21-241990Fig. 6.7 Comparison of the time distribution of the November 21-24, 1990 stormwith time probability distribution curves at (a) Vancouver Harbour(b) station 1 OA and (C) station 1 4A1590 20 40 60 80 100CUMULATIVE PERCENT OF STORM DURATIONCHAPTER 7APPLICATION OF A METEOROLOGICAL MODEL7.1 IntroductionIn the previous Chapters, the study of precipitation was carried out by statistical analysis ofthe existent database. The findings of this analysis are then generalized over the coastalregion of British Columbia and relationships between the short-term and the long-termprecipitation were identified to extend the application of the results over the region. Anotherway to estimate precipitation is by using meteorological models. Several theoreticalmeteorological models have been proposed which can evaluate the short-term precipitationconsidering the appropriate meteorological and topographic parameters and terrain-atmospheric interactions. The basic approach uses a wind model to calculate the horizontaland vertical air motions induced by the mountain terrain. This wind model is then used todrive the rain model which estimates the condensation from the moist air mass as it passesover the mountain regions.The wind field can be regarded as the combined effect of three major factors: synoptic-scale forcing, topographic blocking or channeling, and thermal effects. The synoptic-scalepressure field itself can be greatly modified by the dynamic and thermodynamic effects oflarge-scale topography. Relatively simple models suitable for the diagnosis or forecasting ofthe wind field have been used and can be grouped into three types according to the approachadopted: mass conservation models, models using one-layer vertically integrated primitiveequations of motion and models using one-level primitive equations. Mass conservationmodels (Dickerson, 1978; Ross et al., 1988) assume a well-mixed, constant density layerbeneath a low-level inversion where mass is conserved. The second simplified model(bOChapter 7. APPLICATION OFA METEOROLOGICAL MODELapproach uses the equations of motion, vertically integrated for a well-mixed boundary layer(Lavoie, 1974; Overland et aL, 1979). In these models mass is conserved in the mixed layerbut not the layers above. The third type of model uses the primitive equations for one levelwithout a continuity equation. For example, Danard (1977) proposed a model which requiresthe geostrophic wind at the surface and at 850 mb, the lower tropospheric lapse rate andsurface air temperature. It integrates to a steady state the tendency equations at the surfaceonly for wind, pressure and potential temperature. Mass and Dempsey (1985) extend thatapproach to calculate surface wind and temperature using equations for horizontal momentumand temperature tendency in sigma coordinates. The wind field is determined by the verticaltemperature structure. Thermally-induced circulations due to diabatic forcing can also beincluded. The model has been applied at southwestern coastal British Columbia, westernWashington and north-western Oregon.The above types of wind models require a modest amount of initial data andcomputing resources and are useful for analysis and forecasting of wind for engineeringpurposes. However, the current trend is to embed mesoscale models capable of resolvingregional detail in general circulation models (Giorgi and Bates, 1989; Giorgi, 1990). Thistype of approach will be useful for climate assessments and scenarios of changed forcings.The estimated wind field is used as input to a precipitation model for the estimation ofprecipitation field over complex terrain. Modeling of orographic precipitation has followedtwo broad lines of approach. Some analytical studies (Elliot and Shaffer, 1962; Danard,1971; Rhea, 1978) have used a combination of the Bernoulli equation, the continuity equationand hydrostatic flow for mountains of arbitrary shape. Other analyses (Walker, 1961; Wilson,1978) have been based on the perturbation method and idealized barriers. In this approach themotion in a (x, z) plane can be expressed as a perturbation superimposed on a steady basiccurrent of velocity.161Chapter 7. APPUCATION OF A METEOROLOGICAL MODELThe essential components of any type of orographic precipitation models includemeasures of the adiabatic ascent or descent, condensation or evaporation, and precipitation ofthe condensate. The treatment of water substance in the above models is quite variable. Insome models, all of the condensed moisture is precipitated, others use various precipitationefficiency factors (Marwitz, 1974). In some models (Young, 1974 and Nickerson et al., 1978)cloud microphysics is also incorporated.Both the wind models and the precipitation models can be set up in three dimensionsbut in most cases a vertical cross section can reasonably represent the storm\u2019s flow pattern intoa project basin. This reduction to two dimensions simplifies the calculations and reveals therelevant factors in storm precipitation (Wiesner, 1970). Furthermore, the meteorologicalmodels can simulate the physical processes over the full-atmosphere or just in the boundary-layer of the atmosphere which extends a few kilometers over the terrain. Even thoughprecipitation is generated several kilometers above the earth\u2019s surface, the horizontal variationsof precipitation should be correlated to horizontal variations of the physical processes in theAtmospheric Boundary Layer (ABL). These models are flexible, and more simple andsuitable for engineering design than the full-atmosphere models. In addition to the simplicityanother advantage of boundary-layer models compared to full-atmosphere models is that theformer appear to be relatively free of truncation errors associated with using the sigmacoordinates in steep terrain (Danard and Jorgensen, 1992). Hence boundary-layer modelscan, hypothetically, be used with very small grid sizes in a mountainous terrain.This Chapter presents the results of the application of a boundary-layer meteorologicalmodel in the study area north of Vancouver. The model is the BOUNDP model and it istested in an area which has a highly variable topography. Furthermore, an additional test ofthe model is the small grid size. The model will be used to simulate historic storms, and thento predict the precipitation over the area for particular storms. The purpose of this testing of162Chapter Z APPLICATION OF A METEOROLOGICAL MODELthe model is to evaluate its performance and to identify whether it is suitable for theforecasting of runoff from mountainous watersheds of the region if it is combined with ahydrological model or whether it can be used for the estimation of the Probable MaximumPrecipitation and consequently, the Probable Maximum Flood.Firstly, a brief description of the model will be given. Then, the input data necessaryfor the application of the model will be discussed. The results of the application of the modelwill be presented in the next section. Finally, the concluding remarks will be stated.7.2 General Description of the BOUNDP Model7.2.1 OverviewThe meteorological model BOUNDP was designed by Danard and Jorgensen (1992). Themodel was used in this study because it was readily available with advice and help from Dr.Danard and it has been used in hydrological applications by the British Columbia HydropowerAuthority. The test of the model is the first independent test of the model against observeddata.The model consists of two main parts, the calculation of surface winds and thecomputation of the vertical flux of water at the top of the Atmospheric Boundary Layer(ABL). These two parts will be briefly described in the next paragraphs.163Chapter 7. APPLICATION OF A METEOROLOGICAL MODEL7.2.1.1 The wind modelThe wind model is adapted from the models designed for British Columbia Ministry ofForests (Danard and Galbraith, 1991) and the U.S. National Weather Service (Danard andGalbraith, 1989) and has been presented in detail in two papers (Danard, 1988 and Danard,1989). The wind model calculates the geostrophic wind Vg which is the air movementresulting when the pressure force and the Coriolis force are in balance. The geostrophic windis the result of the pressure gradient and the rotation of the earth assuming no friction and notopography.From the geostrophic wind Vg the model calculates the large-scale velocity u* usingthe expression:u*=C8V (7.1)whereg[in( A] + B2is the geostrophic momentum transfer coefficient (square root of the conventional dragcoefficient), k is the von Karman\u2019s constant (0.35), h is the height of the atmosphericboundary layer (ABL), z0 is the roughness length, and A and B are universal generalizedsimilarity functions.The height of the Atmospheric Boundary Layer is calculated from the formulaproposed by Brown (1981):164Chapter 7. APPLICATION OFA METEOROLOGICAL MODELh=cbj (7.2)where f is the Coriolis parameter and eb is a dimensionless factor less than 0.3 for stableconditions (L>0) and greater than 0.3 for unstable conditions (L<0).The universal generalized similarity functions, A and B, are calculated using themethod of Danard (1988).The component wind which is necessary for the calculation of the vertical water flux isthe balanced surface wind . is the unaccelerated wind for which the large-scale pressuregradient, the Coriolis and the frictional forces are in balance. The balanced surface windspeed is estimated by:=\u00e7[1fl[]+f2[]] (7.3)where uK is the large-scale wind velocity, z0 is the roughness length over water L is theMonin-Obukhov length, Za is the anemometer height andf2(za\/L) is a stability correction termand k is the von Karman\u2019s constant (k=0.35).The first law of thermodynamics is applied to the surface in the form:L=_v.vo+Kv2(e_o)+Q_c(e_e) (7.4)where 9 is the surface potential temperature, Kt is the horizontal thermal diffusivity, 8 is theinitial surface potential temperature, Q is the diabatic heating rate, C is a nudging coefficient,165Chapter Z APPLICATION OF A METEOROLOGICAL MODELand 8\u00e7 is the large-scale potential temperature. The diabatic heating rate, Q, can be foundassuming that the surface pressure tendency is hydrostatic.Then, the equation of motion is integrated in the form:(7.5)where 9 is the surface wind at any level in ABL, Z is the terrain elevation, R is the gasconstant, T is the surface air temperature, g is the acceleration of gravity, p5 is the surfacepressure, f is the Coriolis parameter, k is the von Karmans constant, P is the frictional forcepet unit mass in the surface layer and it is assumed = Cf . 92, Km is the momentumhorizontal diffusivity, C is a nudging coefficient, and is the balanced wind the speed ofwhich is given in Equation 7.3.The estimated wind field around and over the mountains is estimated solvingEquations 7.4 and 7.5 and then, this wind is used as input to the water flux model whichapproximates the condensation processes in the ABL. The water flux model will be brieflypresented in the next paragraphs.7.2.1.2 The water flux modelThe basic predictor is the vertical flux of the water at the top of the Atmospheric BoundaryLayer (ABL). The vertical flux can be written as:W=Wb+E (7.6)166Chapter 7. APPLICATION OF A METEOROLOGICAL MODELwhere = (r + r )pv is the non-turbulent flux of water (or undisplaced flux), r is the watervapour mixing ratio, rj is the mixing ratio of condensed water (having a value of 5x104), p isthe air density, VP is the vertical velocity relative to an isobaric surface and E is theevaporation from the earth\u2019s surface. Wb is usually larger than E.It can be proven (Danard and Jorgensen, 1992) that the vertical velocity can be writtenas:(7.7)with:gp dtV0 = -\u2014aHYHVPSgpVpVdogp Hwhere g is the acceleration of gravity, p is the air density, p is the surface pressure, 0\u2019H is thevalue of the sigma coordinate a = -, at the top of the ABL (aH 0.9), and V is the wind atthe top of the ABL.The term V of the above equation represents the effect of the surface pressuretendency and it is usually small. The term V0 is the effect of the orography (upslope, anddownslope motion) and it is positive as air is moving from high pressure (low elevation) tolow pressure (high elevation). Finally, the term V represents the effect of convergence due tofriction or orography.When the atmospheric water vapour is displaced vertically upwards, it takes some timefor it to condense and to grow to precipitation size and begin falling. Precipitation dropletsare carried with the winds as they fall. Furthermore, the precipitation is initially in the form167Chapter 7. APPLICATION OF A METEOROLOGICAL MODELof ice at the top layers of ABL even for summer storms. The ice particles can be transportedover a very large distance by the wind. The vertical water flux estimated by the Equations 7.6and 7.7 is the undisplaced water generated at each grid ignoring the movement of the dropletsdue to horizontal wind. In order to account for the horizontal movement of the precipitationdroplets, a routine for the estimation of the downwind displacement of the water flux has beenincorporated in the model (displaced water flux).7.2.2.3 Estimation of precipitationOnce the values of the displaced vertical water flux W are calculated, they are then fitted tothe observed precipitation P at a number of stations, through regression. Various relationshipscould be used. The first is:P=A1+2W (7.8)where A1 and A2 are regression coefficients. The coefficient A1 represents the precipitationthat occurs over a horizontal smooth surface with no topography.Another relationship is:P=4+A2w+3 (7.9)which has been shown to account for the effects of W on duration as well as on the intensity(Danard, 1971).Another alternative relationship is:168Chapter 7. APPLICATION OFA METEOROLOGICAL MODELP=A+A2W+3+4Z (7.10)where Z is the terrain elevation. This equation also accounts for the small topographicvariations. The efficiency of the above equations will be tested in the application of themodel.The meteorological input data are available every 6 hours from the CanadaMeteorologic Centre. However, the model averages the vertical water flux over a 24-hourperiod for each of the four 6-hour time steps. The average 24-hour vertical water flux is usedin Equations 7.8, 7.9, and 7.10.Finally, the model uses an objective analysis procedure in order to minimize thediscrepancies between the estimated and observed precipitation (Danard and Jorgensen, 1992).The result is called objectively analyzed precipitation and it should be similar to the observedprecipitation.7.3 Data SetsThe meteorological model BOUNDP has been applied to the North Shore mountain areawhich covers the two study watersheds, the Seymour River and Capilano River watersheds.The data sets required by the model for its application will be presented in the nextparagraphs.Two types of data are required by the model: a) data that are necessary to initialize themodel run and b) data that are used throughout the running of the model. The initial datarequirements include: i) terrain elevation (mean elevation of each grid cell), ii) waterpercentage of each grid cell, iii) water temperature, iv) ice percentage of each grid cell.Topographical data have been digitized for the whole Province of British Columbia by the169Chapter 7. APPLICATION OF A METEOROLOGICAL MODELBritish Columbia Department of Environment, Land and Parks. The terrain elevations aresupplied for a grid 30\u201dx30\u201d, and then are averaged for the model grid cells. The waterfraction is digitized from topographical maps of 1:50,000 scale. The water temperature is notnecessary input data and is calculated by the model. The ice-fraction is considered to be zerofor the storms simulated in this study.Data used throughout the running of the model are: i) height and temperature at 700,800, and 1000 mb, pressure levels and ii) boundary-layer relative humidity. The heights andtemperatures at 700, 850, and 1000 mb were retrieved from the Canada Meteorological Centre(CMC) for a grid of l\u00b0xl\u00b0. These data are the output of the CMC finite element model. Thismodel accepts radiosonde measurements usually every 24 hours from a very sparse radiosondenetwork across Canada. The model then interpolates and forecasts the meteorologicalelements every 6 hours for the next 24 hours for a grid of 1\u00b0xl\u00b0. When a new set ofmeasurements are available the model updates the forecasts of these data. The 24:00 UTC(Universal Coordinated Time) data for each day are the updated data and the 06:00 UTC,12:00 UTC, and 18:00 UTC data are the forecast data. These data are available for 5 days aweek, from Monday to Friday. For the weekends, only the forecast output of the CMC finiteelement model is used.The meteorological data of l\u00b0xl\u00b0 needs to be interpolated, once more, to theBOUNDP model grid. In this study, the interpolation is achieved by using B-splines (IMSL,1989). Fourth order polynomials are used for both latitude and longitude for the interpolation.The meteorological data, retrieved from CMC, contained no information about relativehumidity. To compute the relative humidity, the Equation 7.11 is used (Linsley et a!, 1982):RH=lOO(h1201T+Td\u20191 (7.11)1l2+0.9T )170Chapter 7. APPLICATION OF A METEOROLOGICAL MODELwhere T is the air temperature, and Td is the dew point temperature.The mean daily temperature of the Vancouver Harbour A.E.S. station is used as the airtemperature. The dew point temperature cannot be smaller than the minimum temperature forhumid air. Quick (1987) has used the minimum daily temperature as the dew-pointtemperature for the calculation of smowmelt with success. This assumption has been adoptedin the present study.The Equation 7.11 approximates the relative humidity to within 0.6% in the range of -25\u00b0C to 45\u00b0C (Linsley et al, 1982). Furthermore, the value used in the simulation is thesurface value whereas the average relative humidity of ABL is required. However, it isassumed that Equation 7.11 gives an average value over the domain since upsiope areas havelarger relative humidity than downslope areas. Moreover, testing of the model with variousvalues of relative humidity showed that the model is insensitive to the humidity and itsvariations.7.4. Application7.4.1 ComplicationsThe precipitation was initially simulated for an area covering a latitude range from 49\u00b0 15\u2019 to49\u00b0 35\u2019 and a longitude range from 123\u00b0 18\u2019 to 122\u00b0 48\u2019. The calculation domain consisted of20x20 grids each having dimensions l\u2019xl\u2019 or l.2x1.8 km. approximately. The modelcalculates the water flux in the grids located at the edge of the calculation domain, using thevalues of water flux from grids outside the calculation domain. Hence, five grid widthsaround the calculation domain have been added increasing it to the model domain, whichcovered an area from 49\u00b0 10\u2019 to 49\u00b0 40\u2019 latitude and from 123\u00b0 10\u2019 to 122\u00b0 50\u2019 longitude.171Chapter 7. APPLICATION OF A METEOROLOGICAL MODELThe model for this very fine grid mesh gave very large values of vertical water flux.The water flux was in the range of 10,000-100,000 mm\/day. The water flux should be in therange of hundreds of mm\/day. In view of these unreasonable results, the number of the bordergrids was increased from five to fifteen grids increasing the distance of the border around thecalculation domain from 9 km to 27 km in the longitudinal direction and from 6 to 18 km inthe latitudinal direction. This was done because the model interpolates outside the modeldomain resulting in very large vertical fluxes. However, application of the model to theincreased model domain gave similar results. No further attempt was made to increase theborder grid size because the model then would have become inefficient, having a modeldomain about twice the calculation domain.The small grid size gives a much better description of the topography of the area butresults in very steep slopes. It is believed that these steep slopes resulted in numericalinstability and consequently, in unreasonable results. The elevation from one grid to the nextcould be increased by more than 1000 m. This large elevational increase is, for the model,like an infinite increase in elevation between grids. In this case, the model produces verylarge values of the water flux.The next step was to increase the grid size. Increasing the grid size results insmoothing of the topography, giving smaller slopes. The grid size was increased to 2\u2019x3\u2019,which is 3.6x3.6 1cm, approximately. The precipitation was then simulated for an areacovering a latitude range from 49\u00b0 15\u2019 to 49\u00b0 35\u2019 and a longitude range from 123\u00b0 18\u2019 to 122\u00b048\u2019. The new calculation domain consisted of lOxlO grids. Six grids around the calculationdomain have been added, increasing it to the model domain, which covers an area from 49003\u2019 to 49\u00b0 47\u2019 latitude and from 123\u00b0 36\u2019 to 122\u00b0 30\u2019 longitude.The application of the model to the new model domain gave more reasonable waterflux values. The above problems in the application of the BOUNDP model prove that the172Chapter 7. APPUCATION OF A METEOROLOGICAL MODELmodel is affected by the grid size and consequently, the steepness or smoothness of the modeldomain is a very important factor. Small grid size results in numerical instabilities givingunreasonable results.Figures 7.1 and 7.2 are the three dimensional topographical maps of the calculationand model domain for the grid size (2\u2019x3\u2019) used in the modeling, respectively. Thetopographical contour maps are shown in figures 7.3 and 7.4.The results that will be discussed in the next paragraphs are the results obtained fromthe application of the model to the domain of 2\u2019x3\u2019 grid size which gave the acceptable results.7.4.2 ResultsThe meteorological data necessary for the application of the model where readily available forthe years 1990-1992. Seven large historical storms from this period were selected for theapplication of the model BOUNDP. The storms used are: August 29-30, 1990, October 24-27, 1990, November 8-13, 1990, November 21-24, 1990, April 3-4, 1991, August 26-30,1991, November 16-18, 1991. The daily accumulations at 33 stations in the area (Table 7.1)during these storms where used to compare the computed to the observed precipitation. Thefirst four storms were used to estimate the regression coefficients of the Equations 7.8, 7.9,7.10 (Calibration). The next three storms were used for the application of the model in fullprognostic mode, using the analyzed regression coefficients of the historic storms(Verification).Care is needed when comparing the modeled and observed precipitation because themeteorological input data and the modeled precipitation are referred to Universal CoordinatedTime (UTC) whereas the observed precipitation is referred to Pacific Standard Time (PST).Observed climate day for class 1 A.E.S. station begins at 08:00 PST (16:00 UTC) and ends at173Chapter 7. APPLICATION OF A METEOROLOGICAL MODEL08:00 PST (16:00 UTC) of the next morning. To compute the vertical flux for A.E.S.stations, the model is run with input data for 12:00 UTC of the day and 00:00 UTC of the nextday. The resulted displaced vertical water fluxes were averaged to produce climate dayvalues.7.4.2.1 Calibration of the modelAlthough four storms were used for the calibration of the model, only the results for thesimulation of the November 10, 1990 will be shown. These results are the best resultsachieved throughout the calibration procedure. The storm impinged on the area on November8-13, 1990 causing severe floods in the Greater Vancouver Area as has been discussed inChapter 6. The largest daily precipitation was observed on November 10, 1990 at theSeymour Falls Dam station. The 300 mm recorded is the second largest daily accumulation in64 years of record.Figures 7.5 and 7.6 show the undisplaced vertical water flux for November 10 (12:00UTC) and November 11(00:00 UTC), respectively. The undisplaced vertical water flux forthese two days has a similar pattern but larger values of water flux are observed for November11(00:00 UTC). Figures 7.7 and 7.8 show the downwind displaced water flux for November10 (12:00 UTC) and November 11(00:00 UTC). The distribution patterns of the downwinddisplaced water flux for these two days are totally different and the values of the fluxincrease considerably on November 11 (00:00 UTC). Around that time the heaviestprecipitation was recorded.174Chapter 7. APPLICATION OF A METEOROLOGICAL MODELTable 7.1. Precipitation stations used in the application of the BOUNDP modelStation Name ID number* Latitude Longitude Elevation(m)BURNABY CAPITOL HILL 1101146 49 17\u2019 122 59\u2019 183BURNABY METROTOWN 11OA1ND 4913\u2019 123 00\u2019 125BURNABY MTN TERMINAL 1101155 49 16\u2019 122 56\u2019 137BURNABY SIMON FR. UNIV. 1101158 49 17\u2019 122 55\u2019 366IOCO REFINERY 1103660 49 18\u2019 122 53\u2019 53COQUITLAM COMO LAKE AV. 1101889 49 16\u2019 122 52\u2019 160PORT MOODY GLENAYRE 1106CL2 49 17\u2019 122 53\u2019 130VANCOUVER HARBOUR 1108446 49 18\u2019 123 07\u2019 0VANCOUVER KITSILANO 1108453 49 16\u2019 123 10\u2019 12VANCOUVER UBC 1108487 49 15\u2019 123 15\u2019 87N.VANC.DOLLARTON 11ONFNF 4919\u2019 122 57\u2019 52N.VANC.ORANDBOUL. 110EF57 4919\u2019 12303\u2019 111N.VANC.GROUSE MTN RES. 1105658 49 23\u2019 123 05\u2019 1128N.VANC.HIGHLANDS 11OEFNN 4921\u2019 12307\u2019 130N.VANC.CLEVELAND DAM 110EF56 4922\u2019 123 06\u2019 157N.VANC.LONSDALE 1105665 49 19\u2019 123 04\u2019 308N.VANC.REDONDA DR. 1 10N6F5 49 22\u2019 123 05\u2019 229N.VANC.WHARVES 1105669 49 19\u2019 123 07\u2019 6N.VANC.2NDNARROWS 1105666 4918\u2019 12301\u2019 4N.VANC.SONORA DR. 11ON6FF 4922\u2019 123 06\u2019 183N.VANC.SEYMOUR HATCH. 110N666 49 26\u2019 122 58\u2019 210N.VANC.SEYMOUR FALLS 1107200 49 26\u2019 122 58\u2019 244W.VANC.CYPRESS PARK 1108828 49 21\u2019 123 15\u2019 155W.VANC.MILLSTREAM 1108840 49 22\u2019 123 08\u2019 381LIONS BAY 1104634 49 28\u2019 123 14\u2019 137S-i UBC 49 28\u2019 122 57\u2019 2401OA UBC 49 32\u2019 123 00\u2019 29314A UBC 4932\u2019 12301\u2019 48821A UBC 49 32\u2019 123 01\u2019 64025B UBC 4933\u2019 12302\u2019 71628A UBC 4933\u2019 12303\u2019 853C\u2014i UBC 49 26\u2019 123 11\u2019 610C-2 UBC 49 27\u2019 123 06\u2019 320*Qfficjal A.E.S. Station Number175Chapter 7. APPLICATION OF A METEOROLOGICAL MODELThe downwind displaced vertical water flux is used for the estimation of precipitation.The daily observations of 33 stations in the greater study area were used for the fitting ofEquations 7.8, 7.9, 7.10. Table 7.1 shows the stations used and their topographical andgeographical characteristics.Each one of the Equations 7.8, 7.9, 7.10 were used for all four historic storms. Theresults showed that Equation 7.10 gives a better explanation of the variation of precipitation inspace, so that it was decided only to use this equation for the verification of the model.Figure 7.9 shows the model estimated precipitation using Equation 7.10 for November10, 1990. Figure 7.10 shows the objectively analyzed precipitation for the same day. Figure7.1 la is the scatter graph of the observed and calculated precipitation for November 10, 1990.Figure 7.1 lb is the scattergraph of the total observed and calculated precipitation for the stormNovember 8-13, 1990.It is clear, from Figure 7.11, that the model underestimates the high precipitation andoverestimates the smaller precipitation which is observed at the lower elevations. The highvalues of precipitation for November 10, 1990 were observed in Seymour valley, whereincreased convergence generates large amounts of precipitation. The model precipitation forthis particular position is underestimated by more than 100%. Correlation analysis betweenthe estimated and observed precipitation for November 10, 1990 showed that the correlationcoefficient is 0.807. However, the regression line is flat and its slope and intercept isstatistically significantly different from the line of perfect agreement (1:1 line) at 5% level(Fig. 7.lla). The results improved when the total storm precipitation of November 8-13,1990 is considered (Fig. 7.1 ib). The correlation coefficient between the observed and theestimated precipitation increased to 0.905, but still the slope and the intercept of the regressionline is significantly different from the line of perfect agreement at 5% level. The176Chapter 7. APPLICATION OF A METEOROLOGICAL MODELimprovement is the result of the overestimation of precipitation by the model during the lowerprecipitation days.The application of the model to the historic storms shows that the best possibleprediction is achieved for the larger storms and the storms that result in considerableaccumulations in the lowlands. These storms were deep frontal storms and cause severeflooding in the greater Vancouver area. On the other hand, the model gave very poor resultsfor the smaller storms. The correlation coefficients between the simulated and the observedprecipitation are very low, being between 0.10-0.20.Another general observation is the underestimation of the large precipitation in themiddle Seymour and Capilano valleys, which reaches 100%, and the overestimation of thelower precipitation at the low elevations. This large precipitation results from the increasedconvergence of the incoming air. The topography of the area is so variable that the grid sizesmoothed out the critical topographical features, and so eliminated the causative factors of theincreased precipitation.As a result of the underprediction of the precipitation in the middle valleys, the modeldoes not depict the precipitation distribution pattern found from the analysis of the observedstorm precipitation in the Seymour River watershed. That analysis, in Chapter 4 showed thatthe precipitation always increases up to the mid-position of the valley and then eitherdecreases or levels off.7.4.2.2 Analysis of the regression coefficientsThe coefficients found from the application of the model for the four historic storms wereanalyzed in order to fmd appropriate values for the use of the model in the prognostic mode.177Chapter 7. APPLICATION OF A METEOROLOGICAL MODELThe coefficients A1,A2,A3, and A4 of Equation 7.10 are plotted against the averageprecipitation over the area. Figure 7.12 shows the variation of A1 and A2 with the averageprecipitation. The values of A1 increase linearly, except for three values which are lower thanexpected. Coefficient A2 ranges about a constant value.The next figure (Fig. 7.13) shows that, except for three cases, the value of A3 rangesaround zero for all the values of the average precipitation. The values of coefficient A4increase with the average precipitation over the study area but they do not show a consistentlinear relationship. The coefficient A4 was then plotted against the precipitation differencebetween the Grouse mountain resort station and the U.B.C. station and this caused therelationship to become linear. This might be expected, since A4 is the multiplicator ofelevation in Equation 7.10 and, thus, indicates the effect of the orography on the precipitation.From the above relationships, it is clear that the regression coefficients can bepredicted knowing or estimating the average precipitation over the greater area and theprecipitation difference between the mountains and the lowlands. The values of coefficientA1 were correlated against the average precipitation except for the three outlier values. Theresultant equation is,4=O.98OI (7.12)with R2=0.867 (Fig. 7.14). If the average precipitation av is available from some othersource such as satellite or radar data, the value of the A1 coefficient can be estimated fromEquation 7.12.An average value ofA2=O.293 is used for coefficient A2. The coefficient A3 is putequal to zero. Danard (1971) showed that the quadratic term W2 of Equation 7.10 accountsfor the effect of W on the duration as well as on the intensity. In the study area strong frontal178Chapter 7. APPLICATION OF A METEOROLOGICAL MODELsystems generate the large storms. These storms cover large areas having small to moderateintensity and large duration. The areal variation of the intensity and the duration is not aslarge as it is in convective storms. This probably explains the average value of zero of thecoefficient A3.The values of A4 were correlated with the precipitation difference between Grousemountain resort station and U.B.C. station. The resultant equation is:A4\u00b06962Grouse\u2014UBC (7.13)withR2=0.870 (Fig. 7.15).7.4.2.3 Verification of the modelThe storms of April 3-4, 1991, August 26-30, 1991, and November 16-18, 1991 were used forthe application of the model in prognostic mode. The results of the best simulation, that ofAugust 26-30, 1991 storm will be presented in the next paragraphs.Figures 7.16 and 7.17 show the undisplaced flux of August 29, 1991 (12:00 UTC) andAugust 30, 1991 (00:00 UTC). It seems that the general pattern is the same in both figures.Figures 7.18 and 7.19 show the downwind displaced vertical water flux for both the abovedates. The pattern is different in these two figures. With the use of the predicted values of theregression coefficients, the precipitation is predicted and compared to the observed values.Figures 7.20a and 7.20b show the scatter graphs between predicted and observed precipitationfor August 29, 1991 and for the storm period August 26-30, 1991. Correlation analysisbetween the observed and simulated precipitation showed that the correlation coefficient forAugust 29, 1991 is 0.589. Furthermore, the regression line between the observed and179Chapter 7. APPUCATION OF A METEOROLOGICAL MODELmodeled precipitation is flat and significantly different from the line of perfect agreement atthe 5% level. The model overpredicts the lower precipitation and underpredicts the highprecipitation that occurred in the mountain valleys by about 50% (Fig. 7.20a).The results improve when the total storm precipitation from August 26 to August 30,1991 is considered. The correlation coefficient between the predicted and observedprecipitation takes a value of 0.690. The improved correlation for the total storm period is theresult of the overprediction of precipitation during the low precipitation days andunderprediction of the high precipitation days. Hence, the total storm precipitation is betterestimated by the model. However, again the regression line is significantly different from theline of perfect agreement at the 5% level (Fig. 7.20b).From the results of the application of the model for the prognosis of storms, it is clearthat the results compare better to the observed precipitation amounts for the high precipitationdays than for the low precipitation days. The correlation coefficients between the observedand simulated precipitation for the high precipitation days are, on average, about 0.600. Thesimulation of the low precipitation days gave poor results with r values around 0.100.However, the regression line even for the high precipitation days was statistically differentfrom the line of perfect agreement. The regression line was usually flat which shows that themodel severely underpredicts the high precipitation accumulations in the valleys andoverpredicts the low precipitation in the lowlands. Hence, the model fails to reproduce thegeneral spatial distribution pattern of precipitation over the greater area that has been observedand described in Chapter 4.Application of another meteorological model, similar to the BOUNDP model, by Rhea(1978) in the Rocky Mountains of Colorado showed that the model gives the best results forridges and high plateaux, but overestimates amounts in narrow mountain valleys andunderestimates for broad intermontane basins. These findings show that this type of model180Chapter 7. APPLICATION OF A METEOROLOGICAL MODELdoes not at present describe the complex meteorological conditions which are needed toaccurately simulate the mountainous precipitation.7.5 SummaryThe application of the model BOUNDP in the study area showed that the model is verysensitive to the grid size of the calculation domain. Small grid size causes numericalinstability because of the steepness of the terrain. The numerical instability was eliminatedwhen the grid size was increased from l.2x1.8 km to 3.6x3.6 km. This increased grid sizesmoothed out the critical features of the topography which are responsible for the generationof the precipitation in the valleys. As a result the model underpredicts the high precipitationwhich occurs in the mountain valleys. This large accumulation of precipitation is the result ofthe funneling of the incoming air mass and the resultant increased convergence. Furthermorethe model overpredicts the low precipitation in the lowlands so that it fails to reproduce theareal precipitation distribution over the area.The model overpredicts also the precipitation during the low precipitation days andunderpredicts the precipitation during significant accumulations. When the total stormprecipitation over a number of days is considered the overprediction of the low precipitationand the underprediction of the high precipitation are partially compensated.The real test of the model for hydrologic applications will be to use the model inconjunction with a hydrologic watershed model for the simulation of the runoff from themountainous areas. However, the very large underprediction of high precipitation, by even100%, and the misrepresentation of the areal precipitation pattern negate this testing. Theunderprediction of the large storms reduces the reliability of the model for flood modeling.181Chapter Z APPUCATION OF A METEOROLOGICAL MODEL\\ \/ \\ \\ \\2.\\\\\\ )-N5 =_10<0)\u2018-,I\u2022I\u2014 cz \u2022c\u2014It)C) a)Ui zC)Dt \u2014__11SSI I::I I I I\u2022 U C) It) CJ It) It)C., 102691400\u2022OBSERVEDFLOW1300PROPOSEDMETHODpo1200-++BAYESIANMETHOD(RUSSEll..1982)1ioo-xB.C.ENViRONMENTMEflIOD(REKSTEN,1987)x>INDEXFLOODMETHOD1O0O-INDEXFLOODMErHOD-95%x-900-\u2014\u2014-RDPMETHODEVDROMETHOD+xw800-C(I,4\u2014----700-600-.--500-+\u2022\u2022400-300-$-\u2022x0III25102050100RECURRENCEINTERVAL(years)Fig.827.Comparisonof thefrequencyoftheobservedinstantaneouspeakflowwiththefrequencyofthesimulatedinstantaneouspeakflowusingvariousmethodsforSantaRiver.I...GRECURRENCEINTERVAL(years)Fig.8.28.ComparisonofthefrequencyoftheobserveddailypeakflowwiththefrequencyofsimulateddailypeakflowusingvariousmethodsforSaritaRiver.\u2022OBSERVEDFLOWPROPOSEDMETHOD+BAYESIANMETHOD(RUSSELL,1982)xB.C.ENViRONMENTMETHOD(REKSTEN, 1987)INDEXFLOODMETHODINDEXFLOODMEFHOD-95%\u2014\u2014-RDPMETHODvDRQMETHODa, go w C, C) Co900800700600500400300200100 0V\u2014&\u2014ri V\u2014.\u2014\u2014H 02IIIII5102050100CHAPTER 9CONCLUSIONS AND RECOMMENDAflONS9.1 ConclusionsThe primary goal of this Thesis is to study the precipitation distribution in the mountainouscoastal British Columbia and to use the results for the development of techniques for thereliable estimation of flood frequency for ungauged watersheds. This goal is achieved bycombining results from each of the Chapters presented in this Thesis. Study componentsinclude the analysis of the long-term and short-term precipitation in two study watersheds, theSeymour River and Capilano River watersheds; generalization of the findings of the analysisto coastal British Columbia; study of extreme historical storms; application of ameteorological model for the estimation of short-term precipitation; and development of aphysically-based stochastic-deterministic procedure which incorporates the findings of theprevious research on precipitation and runoff generation for the estimation of the floodfrequency from ungauged watersheds of the region. To illustrate the continuity between thestudy components, an overview of the results is included below.The background information about the climate of coastal British Columbia and thetopography of the study area have been presented in Chapter 2. It has been shown that mostof the precipitation in the region is generated during winter and fall months from frontalsystems that are developed over the North Pacific Ocean and travel eastward towards the coastof British Columbia.The study begins with the analysis of the distribution of the long-term precipitation,namely annual, seasonal, and monthly and its distribution with elevation. The analysis showsChapter 9. CONCLUSIONS AND RECOMMENDATIONSthat the annual and the wet period October to March precipitation increases with elevation upto about 400 m in the Capilano River watershed whereas the topography of the Seymour Riverwatershed reduces the elevation to about 260 m. The position of the maximum precipitation isthe middle of the watersheds. After that point the precipitation either decreases as in theSeymour River watershed or levels off as in the Capilano River watershed. On the other hand,the dry period April to September precipitation is more uniformly distributed over the studywatersheds and it is not affected by the elevation.The Bergeron two-cloud mechanism has been identified as the mechanism whichgenerates most of the precipitation in the region, and can explain the distinctive precipitationdistribution observed in this study.Another important finding of the study is that the valley and the adjacent mountainslope precipitation is similar at the same distance from the beginning of the mountain region.This result is very significant because most of the precipitation stations are located in theeasily accessible river valleys. However, this distribution pattern has been observed in the twostudy watersheds only for the initial topographic rise because of the absence of high elevationdata in the back range. It is therefore important that high elevation stations should be installedon the mountain slopes beyond the front range in order to confirm or deny the observationsfor the front mountains.Regional precipitation and runoff data were used to examine the generality of thefindings of the study of the precipitation distribution in the two study watersheds. Thisanalysis showed that the initial results of the study are more general and regional in scale, andthat long-term precipitation follows a similar pattern to that in the two study watersheds,increasing up to about 400-800 m elevation and then either leveling off or even decreasing athigher elevations. This finding is very important because it is usually assumed (Barry, 1992)that the precipitation in the mid-latitude mountainous areas increases almost linearly with273Chapter 9. CONCLUSIONS AND RECOMMENDATIONSelevation up to the top elevation. This result has a very strong impact on the design andplanning procedures of water resources of the region.The next step was to study the short-term or storm precipitation in the Seymour Riverwatershed. This short-term precipitation is one of the necessary components of the floodestimation procedures. The analysis of 175 storms for seven stations showed that the averageprecipitation follows a distribution pattern similar to the pattern found in the analysis of thelong-term precipitation. This fmding is very important since in coastal British Columbia onlyabout one third of the existing precipitation stations are recording gauges capable ofmeasuring the short-term precipitation. This preliminary result suggests that the long-termprecipitation may be used as an indicator of the shorter-term precipitation, but further study ofthis issue is necessary. Moreover, the distribution pattern of the storm is not affected by thetype of precipitation, whether rain, rain and snow or snow.A part of the storm precipitation study was the analysis of the time distribution of thestorms. This analysis showed that the storm time distribution is reasonably constant and doesnot vary significantly with the type of precipitation, elevation, storm duration, and stormdepth. Also, examination of regional data from sparsely located coastal British Columbiastations showed that the storm time distribution does not change significantly over the region.This result indicates that the storm time distribution found in this study can be transposed overthe whole region.The final goal of the research program is to find techniques to accurately estimate theflood runoff from ungauged watersheds of the region, even when the data are limited. Toassist in the estimation of flood runoff, the 24-hour design storm has been developed. Thechoice of the 24-hour storm duration was based on climatic, hydrological, and pragmaticreasons. The design storm has been developed with data from the Seymour River watershedand then it has been compared with other regional studies and data. This comparison showed274Chapter 9. CONCLUSIONS AND RECOMMENDATIONSthat the results for the Seymour River watershed do not differ significantly from the regionaldata, so that they can be transposed over the region. An event-based rainfall-runoff simulationwas undertaken for a real watershed, the Santa River watershed on Vancouver Island, andshowed that only the 10% time probability distribution curve and the synthetic SoilConservation Services type IA hydrograph are capable of accurately reproducing the floodhydrograph.Another important finding of this analysis was that the 24-hour annual rainfall of agiven return period is a constant percentage of the mean annual precipitation. This result isvery important because it expands the results of this study both in space and in time sincedaily data is available from the storage precipitation gauges in coastal British Columbia. Alsothere are more storage gauges and they have longer records than the recording gauges. Thisresult also suggests that the extreme 24-hour annual rainfall probably follows a similar patternto that of the annual precipitation which is important for the estimation of the spatialdistribution of the design storm.The above findings for the short-term precipitation were examined for five extremeflood producing historic storms that occurred over the two study watersheds. This analysisshowed that the results of the storm precipitation analysis are valid for the extreme storms,which adds confidence in their use in coastal British Columbia.A theoretically-based meteorological model, the BOUNDP model, was tested to checkwhether it could predict the precipitation distribution and to confirm the results of thestatistical analysis. The model was applied for the mountainous area of the two studywatersheds, Seymour River and Capilano River. From the comparison of the model resultswith the observed precipitation, it was evident that the model is incapable, in its present form,of simulating the large precipitation amounts observed in the mountain areas and ofreproducing the distinct precipitation distribution pattern found in this study. As a result, no275Chapter 9. CONCLUSIONS AND RECOMMENDATIONSattempt was made to use the model for hydrologic modeling of the runoff from thesemountainous watersheds, as was initially intended.The final step of the research program was to develop a technique to estimate the floodfrequency for ungauged watersheds with limited data. This was achieved using the method ofderived distributions and the integration of the previous results of the precipitation analysisand the study of the watershed response. The proposed method is a physically-based methodbecause all its parameters can be estimated by using physical variables, and it is stochastic-deterministic because it uses a deterministic watershed response model which has stochasticparameters, along with a stochastic rainfall generation model.The proposed procedure was applied to eight coastal British Columbia watersheds andcompared with other regional techniques. The results showed that the method is easy toapply, requires very limited data, and is efficient and reliable for determining the hourly anddaily peak flows.In summary, this Thesis examines the distribution pattern of precipitation withelevation; provides regional characteristics of long-term and storm precipitation for estimatinginput precipitation data to a hydrologic model; and proposes a physically-based stochastic-deterministic method for the estimation of flood frequency from ungauged watersheds in thecoastal region of British Columbia.9.2 RecommendationsOne of the most important findings of this study is that precipitation does not continue toincrease linearly with elevation, as has often been assumed (Melone, 1986; Barry, 1992).Consequently, water supply and design floods may both be overestimated if the leveling off276Chapter 9. CONCLUSIONS AND RECOMMENDATIONSand even reduction of precipitation above even modest elevations of about 400 to 800 m is nottaken into account.Clearly, this decrease or levelling off of precipitation at higher elevations is a matter ofsuch high economic importance that considerable effort and expenditure should be made toconfirm or deny this result by gathering additional higher elevation data in the region. Thesedata can be used also in the development and testing of more reliable meteorological modelsof mountainous precipitation.Furthermore, these additional high elevation data can also be used to test anotherfinding of this study, namely that the mountain slope and valley precipitation are similar at thesame position. This finding is very important, and needs to be confirmed by expanding thedata base to assist in the reliable evaluation of the areal precipitation, especially as mostprecipitation stations are located in the river valleys.Another topic for further research is the application of the proposed procedure for theestimation of flood runoff to other areas of the coastal Pacific Northwest. Furthermore, theprocedure could be applied to other areas of different climate from that of the coastal BritishColumbia. This application requires appropriate adjustments in the values of the modelparameters. Also, another potential future application of the procedure is in evaluating theimpact of watershed changes on flood magnitudes and frequencies. This evaluation would beproduced for a given watershed with parameter values representing the modified or futureconditions.A last point which is considered significant for further research is the adaptation of theproposed procedure for the estimation of floods generated by rain on snow events. The focusin this research program was on the rain storm produced floods. Melone (1986) has shownthat rain on snow is the second most important mechanism for the generation of peak flow incoastal British Columbia after the rain storms. Also, Melone proved that the increased277Chapter 9. CONCLUSIONS AND RECOMMENDATIONSresponse of the coastal watersheds to these events is not due to a fundamental change of thewatershed behaviour but it is the result of increased water input from the snowmelt.To incorporate these processes in the proposed procedure, a research program shouldbe set up to study the distribution and variation of the snowpack and temperature withelevation. A fundamental part of this type of study would be the identification of rain onsnow events, which can be done if a good coverage of meteorological and hydrologicalstations exists.In conclusion, this Thesis examines the precipitation distribution in space and time atvarious spatial and temporal scales; develops the 24-hour design storm rainfall for coastalBritish Columbia; tests a meteorological mathematically based model in the study area andfmaily incorporates the above fmdings of precipitation analysis and previous results ofwatershed response analysis and modeling into a physically-based stochastic-deterministicprocedure for the estimation of flood frequency from ungauged watersheds of the region. Theresults of this research study are readily applicable for water resources design in coastalBritish Columbia, although they can be expanded and refined more, when additional data areavailable.278REFERENCESAmorocho, J. and B. 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Precipitation Stations in coastal British Columbia.Name Station Elevation Period of RecordNumber* (m)ABBOTSFORD 1100030 58 1944-1980AGASSIZ CDA 1100120 15 1889-1966AIYANISH 1070150 229 1924\u20141971ALBERNIBC 1030180 91 1894-1959ALBERNI LC 1030210 9 1948- 1974ALBERNI ML 1030220 43 1958\u20141973ALBERNIRC 1030185 75 1962\u20141969ALDERGROVE 1100240 76 1953-1980ALERT BAY 1020270 52 1913-1980ALICE ARM 1060330 314 1948\u20141964ALOUETTE L 1100360 117 1924-1970ALTA LAKE 1040390 668 1950\u20141976ALTALAKE2 1040420 640 1931\u20141969AMPHITRITEP 1030425 11 1968\u20141980BALLENAS LIGHT 1020590 11 1966\u20141980BAMBERTON OC 1010595 85 1961\u20141980BAMFIELD EAST 1030605 4 1959-1980BEAR CR 1010720 351 1964-1971BECHER BAY 1010780 12 1956\u20141966BELLA BELLA 1060810 12 1964-1977BELLA COOLA 1060840 18 1939\u20141980BELLA COOLA BC H 1060842 14 1961-1980BENSON LAKE 1030850 145 1959-1972BLACK CREEK 1020880 46 1976- 1980BLIND CHANNEL 1020885 3 1956- 1980BONILLA ISL 1060902 16 1960-1980BOWEN ISL AB 1040908 23 1959\u20141965BOWEN ISL BB 104090R 8 1966\u20141978BRITANNIAB 1041050 49 1913-1974BUNTZEN LAKE 1101140 17 1969-1980BURNABY BR 1101144 125 1959-1970BURNABY CAPITOL 1101146 183 1960-1980BURNABY MTN 1101155 137 1958-1980BURNABY SFU 1101158 336 1965-1980BURQUITLAM 1101200 61 1926-1975CAMPBELL RIV 1021260 79 1936-1969CAMERON LAKE 1021230 198 1924-1980CAMPBELL RIVA 1021261 105 1965-1980CAMPBELL RIV BCFS 1021262 128 1969-1980CAPE LAZO 1021320 38 1935\u20141962300APPENDIX ATable Al. Precipitation Stations in coastal British Columbia. (cont.)Name Station Elevation Period of RecordNumber* (m)CAPE SCOTT 1031353 72 1965\u20141980CAPE ST JAMES 1051350 89 1944\u20141980CARMANAH POINT 1031402 38 1968\u20141980CARNATION CREEK CDF 1031413 61 1971-1980CENTRAL SAANICH ISL 10114F6 38 1970-1980CENTRAL SAANICH V 1011467 53 1970-1980CHATHAM POINT 1021480 20 1958-1970CHEMAINUS 1011500 53 1934-1979CHILLIWACK 1101530 6 1950-1980CHILLIWACKGR 1101545 12 1961-1980CHILLIWACKRFCR 1101565 457 1966\u20141980CFIILLIWACK R MT THURSTON 1101N65 198 1963-1980CHILLIWACK RIV CT 1101564 488 1961-1976CLOWHOM FALLS 1041710 23 1932-1980COAL HARB 1031735 57 1970\u20141980COBBLE HILL 1011745 61 1970\u20141980COMOX A 1021830 24 1944-1980COQUITLAM L 1101890 161 1924-1980CORDOVA BAY 1011920 37 1951\u20141980CORTES ISL 1021950 6 1947\u20141980CORTES ISL TB 1021960 5 1960-1973COURTENAY 1021990 24 1930-1980COURTENAY GR 1021988 81 1972-1980COWICHAN BAY 1012010 104 1953\u20141980COWICHAN LAKE F 1012040 177 1949-1980COWICHAN LAKE WEIR 1012055 163 1960-1980CULTUS LAKE 1102220 46 1962\u20141980CUMBERLAND 1022250 159 1922\u20141980DAISY LAKE DAM 1042255 381 1968-1980DELTA LADNER SOUTH 1102417 2 1971-1980DELTA PEBBLE 1102420 12 1961-1980DELTA TSAWWASSEN 1102424 53 1959-1969DELTA TSAW. BEAC. 1102425 2 197 1-1980DENMAN ISL 1022430 35 1910-1965DUNCAN BAY 1022560 7 1957-1980DUNCAN FOR 1012570 6 1958\u20141980EAST SOOKE 1012628 37 1966- 1980EGG ISL 1062646 12 1965- 1980ELK LAKE 1012655 114 1957\u20141978ESQUIMALT M 1012700 12 1872\u20141950301APPENDIX ATable Al. Precipitation Stations in coastal British Columbia.(cont.)Name Station Elevation Period of RecordNumber* (m)ESTEVAN POINT 1032730 7 1908\u20141980ETHELDA BAY 1062745 8 1957\u20141980FALLS R1V 1062790 18 1931\u20141980GABRIOLA ISL 1023042 46 1967-1980GALIANO ISL 1013045 15 1956\u20141977GAMBlER HAR 1043048 61 1962-1980GARIBALDI 1043060 381 1921\u20141980GIBSONS 1043150 126 1949\u20141980GIBSONS GP 1043152 34 1961-1980GOLD RIV 1033232 117 1966-1980GRAHAM INLET 1203255 660 1973-1980HANEY CI 1103324 142 1960\u20141980HANEY EAST 1103326 30 1959-1980HANEY UBC RF SP17 1103334 373 1961-1972HANEY UBC RFA 1103332 143 1961-1980HANEY UBC RFLL 1103333 354 1962-1972HANEY UBC RFM 11OCCCC 114 1945-1972HATZICPR 1103342 9 1959-1976HELLS GATE 1113420 122 1951\u20141980HOLBERG 1033480 52 1956\u20141980HOLBERG FD 1033483 46 1967\u20141980HOLLYBURN RIDGE 1103510 951 1954-1980HOPE A 1113540 39 1935\u20141980HOPEKL 1113550 152 1955\u20141977HOPKINS L 1043582 8 1969\u20141980IOCO REF 1103660 53 1955\u20141980JAMES ISL 1013720 54 1914-1978KEMANO 1064020 70 1951\u20141980KENSINGTON PR 1104080 27 1953-1978KILDALA 1064138 30 1966\u20141980KILDONAN 1034170 3 1972-1976KINGCOME INLET 1064227 2 1974\u20141985KITIMAT2 1064321 17 1966\u20141980KITIMAT MISSION 1064290 6 1939\u20141948K1TIMAT TOWN 1064320 128 1954- 1980KLEENA KL 1084350 899 1942- 1968KYUQUOT 1034440 3 1933\u20141959LADNER 1104470 1 1952\u20141971LADNER MSTN 1104477 0 1959-197 1LADNER PG 1104484 0 1960-1975302APPENDIX ATable Al. Precipitation Stations in coastal British Columbia. (cont.)Name Station Elevation Period of RecordNumber* (m)LAKELSE LK 1064497 85 1967\u20141980LANGARA 1054500 41 1936\u20141980LANGFORD LAKE 1014530 76 1953-1980LANGLEYL 1104555 101 1957-1980LANGLEY PR 1104560 87 1953-1980LOIS RIV DAM 1044680 131 1954\u20141956LUND 1044732 14 1960\u20141975MALIBU JERVIS INLET 1044840 8 1974-1980MASSET 1054920 3 1897\u20141968MASSET CFS 10549BN 12 1971\u20141980MAYNE ISLAND 1014931 30 1970-1980MCINNES ISL 1064010 23 1954-1980MERRY ISL 1045100 6 1942-1980MESCHOSIN HV 1015107 76 1968\u20141980MILL BAY 1015134 46 1972\u20141980MILNERAIC 1105155 8 1967\u20141979MILNES LANDING 1015160 38 1910-1956MISSION 1105190 56 1957\u20141980MISSION WA 1105192 221 1962\u20141980MOUNT SEYMOUR 1105230 823 1958-1968MUD BAY FRB 1025240 11 1971-1980MUIR CR 1015242 30 1970\u20141980MULE CR 1205248 884 1970-1980NANAIMO 1025340 70 1892-1980NANAIMO A 1025370 30 1947\u20141980NANAIMO CHUB 10253P0 21 1969-1980NANAIMO DEP BAY 1025C70 8 1970- 1980NEW WESTMINSTER 1105550 119 1894-1980NEW WESTMINSTER BCPEN 1105553 18 1960-1980NEW WESTMINSTER W 1105570 84 1960-197 1N.VANCOUVER 2ND NAR 1105666 4 1957-1980N.VANCOUVER CAPILANO 1105655 67 1955-1980N.VANCOUVER CLEVELAND 110EF56 157 1968-1980N.VANCOUVER CLOVERL. 11OEFEF 79 1968-1980N.VANCOUVER HOLYROOD 1105659 183 1958-1968N. VANCOUVER LYNN CR 1105660 191 1964-1980N.VANCOUVER RDR 110N6F5 229 1973-1980N.VANCOUVER SEYMOUR 11OEFFF 9 1968-1980N.VANCOUVER UP.LYNN 1105668 177 1960-1980N.VANCOUVER WHARVES 1105669 6 1962-1980303APPENDIX ATable Al. Precipitation Stations in coastal British Columbia. (cont.)Name Station Elevation Period of RecordNumber* (m)N.VANCOUV. GROUSE 1105658 1128 1971-1980OCEAN FALLS 1065670 5 1924\u20141980OYSTER RIV UBC 1025915 11 1967-1980PACHENA POINT 1035940 46 1924\u20141980PARKSVILLE 1025970 82 1915\u20141960PENDERISL 1016120 15 1924\u20141965PIERS ISL 1016169 0 1973\u20141980PITT MEADOWSL 1106177 6 1960-1969PITT POLDER 1106180 2 1951\u20141980POINT ATKINSON 1106200 9 1968\u20141980PORT ALBERNI 1036205 59 1917\u20141962PORT ALBERNI A 1036206 2 1969-1980PORT ALBERNI CCR 1036207 70 1960- 1980PORT ALBERNI RED 1036210 21 1947-1980PORT ALICE 1036240 15 1924-1980PORT CLEMENTS 1056250 8 1967-1980PORT COQUITLAM 1106255 7 1958\u20141980PORT HARDY A 1026270 22 1944-1980PORT HARDY BHR 1026274 5 1959\u20141975PORT KELLS 1106300 9 1953-1965PORT MELLON 1046330 8 1942-1980PORT MOODY GRFY 1106CL2 130 1970-1980PORT RENFREW BCFP 1016335 6 1970-1980POWELL RIVER 1046390 52 1924-1980POWELL RIVER A 1046391 130 1953-1980POWELL RIVER W 1046410 55 1960-1980PRINCE RUPERT 1066480 52 1908-1963PRINCE RUPERT A 1066481 34 1961-1980PRINCE RUPERT MC 1066488 85 1959-1980PRINCE RUPERT PARK 1066492 91 1959-1980PRINCE RUPERT SH 1066193 11 1966-1980QUALICUM RFR 1026563 8 1962\u20141980QUATSINO 1036570 8 1895\u20141980RIVER JORDAN 1016780 3 1908\u20141980SAANICH DAO 10169DK 223 1916-1977SAANICH DEN 1016942 38 1963\u20141974SAANICTON CDA 1016940 61 1914\u20141980SALT SPRING ISL 1016990 73 1945-1980SALT SPRING IV 1017000 7 1955\u20141980SANDSPIT A 1057050 5 1945\u20141980304APPENDiX ATable Al. Precipitation Stations in coastal British Columbia. (cont.)Name Station Elevation Period of RecordNumber* (m)SARDIS 1107080 107 1954-1980SAYWARD BCFS 1027114 15 1973-1980SECHELT 1047170 23 1927- 1968SEWALL MASSET IN 105PA91 3 1974-1980SEWELL INLET 1057192 12 1973\u20141980SEYMOUR FALLS 1107200 244 1927\u20141980SHAWNIGANL 1017230 137 1918\u20141980SOOKE 1017556 27 1970\u20141980SOOKE LAKE 1017560 173 1913-1966SOOKE LAKE N 1017563 229 1966\u20141980SOUTH PENDER ISLAND 1017610 61 1966-1980SPRING ISL 1037650 11 1949-1979SQUAMISH 1047660 2 1959-1980SQUAMISH FMC CHEMICALS 1047662 3 1968-1980STAVE FALLS 1107680 55 1959-1989STEVENSON 1107710 1 1896-1980STEWART 1067740 5 1926-1967STEWART A 1067742 7 1974- 1980STEWART BCHPA 1067745 12 1967\u20141976STILLWATER PH 1047770 24 1931-1980STRATHCONA DAM 1027775 201 1967-1980SUMAS CANAL 1107785 6 1957-1980SURREYKP 1107873 93 1960\u20141980SURREYMH 1107876 76 1962\u20141980SURREY N 1107878 73 1960\u20141980SURREY S 1107879 101 1960-1980TAHSIS 1037890 5 1952- 1980TAHTSA LAKE WEST 1087950 863 1951\u20141980TASU SOUND 1058003 15 1963\u20141980TATLAYOKO LAKE 1088010 853 1928-1980TERRACE A 1068130 217 1944-1980TERRACE PCC 1068131 58 1968\u20141980TEXADA ISL 1048140 24 1960-1980TLELL 1058190 5 1950\u20141980TOFINO A 1038205 20 1942-1980TUNNEL CAMP 1048310 671 1924-1974UCLUELET KENNEDY CAMP 1038330 12 1914-1948VANCOUVER A 1108447 3 1936-1980VANCOUVER CITY H 1108430 86 1924-1980VANCOUVER DUNBAR 1108435 61 1955\u20141974305APPENDIX ATable Al. Precipitation Stations in coastal British Columbia.(cont.)Name Station Elevation Period of RecordNumber* (m)VANCOUVER HARBOUR 1108446 0 1925-1980VANCOUVER KERRISDALE 1108449 88 1970- 1980VANCOUVER KITSILANO 1108453 34 1956-1980VANCOUVER OAK 53 1108462 82 1970-1977VANCOUVER PMO 1108465 59 1898-1979VANCOUVER SF 1108475 64 1955-1972VANCOUVER SOUTH 1108436 61 1966-1982VANCOUVER UBC 1108487 87 1957-1980VICTORIA A 1018620 19 1940-1980VICTORIAGH 1018614 43 1959-1980VICTORIA GHTS 1018610 69 1898-1980VICTORIA HIGHLAND 1018616 152 1961\u20141980VICTORIAL 1038640 29 1953\u20141962VICTORIA MARINE 1018642 32 1967-1980VICTORIA PS 1O1HFEE 8 1973-1980VICTORIA SHELBOURNE 101QF57 38 1964-1974VICTORIA SS 1O1QEFG 21 1961\u20141973VICTORIA T 1018660 23 1958-1980WANNOCK RIV 1068677 8 1974-1980WHALLEY FN 1108890 84 1958-1980WHITE ROCK 1108910 61 1929- 1970WHITE ROCK STP 1108914 15 1964-1980WHONNOCK 269 ST 1108927 61 1960\u20141975WHONNOCK HILL 1108925 213 1957-1969WILLIAM HEAD 1018935 12 1959-1980WOODFIBRE 1048974 6 1960-1980W.VANCOUVERD 1108829 2 1971-1980W.VANCOUVER M 1108840 38 1961-1980W.VANCOUVER P 1108846 122 1961-1972YOUBOU 1019010 174 1959-1967*QfficjaI Environment Canada station number306APPENDIX ATable A2. Streamfiow gauging stations in the coastal British Columbia.Station Basin Mean Area (km2) Years of RecordNumber* Elevation (m)ANDERSON CREEK 08MH104 46 27 1965-1987ATNARKO RIVER 08FB006 1024 2430 1965-1988BELLA COOLA RIVER 08FB002 673 310 1948-1968BELLA COOLA R.BBC 08FB007 920 3730 1965-1988BINGS CREEK 08HA016 207 15.5 1961-1988CAMPBELL RIVER O8HDOO1 589 1400 1910-1949CARNATION CREEK 08HB048 765 10.1 1973-1988CHAPMAN CREEK 08GA060 680 64.5 1971-1988CHEAKAMUS RIVER 08GA024 1010 287 1925-1947CHEMAINUS O8HAOO1 644 355 1915-1988ENGLISMAN RiVER 08HB002 828 324 1915-1988HIRSCH CREEK 08FF002 820 347 1966-1988JACOBS CREEK 08MH108 483 12.2 1966-1978KANAKA CREEK 08MH076 168 47.7 1960-1988KEMANO RIVER 08FE002 912 583 1972- 1988KITIMAT RIVER O8FFOO1 960 1990 1967\u20141988KOKISH RIVER O8HFOO1 799 290 1927-1941KOKSILAH RIVER 08HA003 493 209 1960- 1988LITTLE WEDEENE R. 08FF003 855 188 1967-1988MACKAY CREEK 08GA061 497 3.63 1974\u20141988MAHOOD CREEK 08MH020 35 16 1927- 1974MAHOOD CREEK 08MH018 34 18.4 1927-1985MAMQUAM RIVER 08GA054 911 334 1967-1986MURRAY CREEK 08MH129 50 26.2 1970-1982NICOMEKL RIVER 08MH105 29 64.5 1966-1984NIMPKISH RIVER 08HF002 802 1760 1928-1935NOONS CREEK MD 08GA065 382 2.59 1977-1988NOONS CREEK PM 08GA052 253 4.4 1965-1975NORRISH CREEK 08MH058 598 117 1960-1988NORTH ALLOUETTE R. 08MH006 478 37.3 1961-1988NUSATSUM RIVERF 08FB005 897 269 1966- 1988OYSTER CREEK O8HDO11 701 298 1974-1988PALLANT CREEK 08DB002 519 76.7 1968-1987RUBBLE CREEK 080A023 673 74.1 1925-1934SALLOOMT RIVER 08FB004 883 161 1965\u20141988SAN JUAN RiVER O8HAO1O 414 580 1960\u2014 1988SARITA RIVER 08HB014 442 162 1950\u20141988SLESSE CREEK 08MH056 1104 162 1957\u20141988STAWAMUS RIVER 08GA064 785 40.4 1972-1988307APPENDIX ATable A2. Streamfiow gauging stations in the coastal British Columbia.(cont.)Station Basin Mean Area (km2) Years of RecordNumber Elevation (m)TSABLE RIVER 0811B024 681 113 1961\u20141988TSITIKA RIVER 08HF004 792 360 1975\u20141988UCONA RIVER 08HC002 589 185 1957\u20141988YAKOUM RIVER 08DA002 351 477 1963\u20141987YORKSON CREEK 08MH097 43 5.96 1965-1977ZEBALLOS RIVER 08HE006 725 181 1960-1988ZYMOETZ RIVER 08EF005 858 2980 1964-1988ZYMOETZ RIVER TER. 08EF003 549 100 1953-1963*Qfficjaj Environment Canada station number308APPENDIX BRELATIONSHIP BETWEEN EXTREME 24-HOUR RAINFALL ANDMEAN ANNUAL PRECIPITATION3CAPPENDIX BTable Bi. Characteristics of the sixty\u2014one stations used in the analysisof the 24\u2014hour extreme rainfall.24-hour Extreme RainfallReturn PeriodMean annual Station (mm)Station Precipitation Elevation 2 5 10 25 50 100(mm) (m)ABBOTSFORD A 1562.9 58 61.7 77.8 88.3 101.8 111.6 121.4AGASSIZA 1727.8 15 73 88.6 98.9 111.8 121.4 131ALLOUETE LAKE 2775.3 117 97.7 117.6 130.8 147.6 159.8 172.1ALTA LAKE 1415.4 668 43 56.2 64.8 75.8 84 92.2BEAR CREEK 3513.9 351 141.8 205.2 247.2 300.2 339.4 378.5BELLA COOLA H 2109.3 14 88.3 113.8 130.6 151.9 167.5 183.1BUNTZEN LAKE 2909.5 17 111.4 151 177.4 210.5 235.2 259.7BURNABY MTN BCHPA 1908.9 465 75.1 89.8 99.6 111.8 121 129.8CAMPBELL R. BCFS 1655.9 128 54 65.3 73 82.6 89.8 96.7CAMPBELL R.A. 1409.1 106 60 67.2 76.8 84 91.2 96CAMPELL RIVER BC. 1406 30 60 69.8 76.3 84.5 90.7 96.7CARNATION CREEK 2770.3 61 91.9 119 137.3 159.8 176.9 193.7CHILLIWACK MICR. 1850.5 229 55.2 66.5 73.9 83.5 90.5 97.4CLOWHOM FALLS 2230 23 78 92.9 102.7 115.2 124.3 133.4COMOX A 1187.6 24 58.1 69.4 77 86.4 93.6 100.6COQUITLAM LAKE 3616 161 143.8 174.5 194.6 220.3 239.3 258.2COURTENEY PUNTL. 1464.7 24 66.2 84.5 96.7 112.1 123.4 134.6DAISY LAKE DAM 2054.2 381 66.2 78.7 87.1 97.7 105.4 113ESTEVAN POINT 3180.6 7 131 168.2 193 223.9 247.2 270HANEY MICR. 1763.9 320 78.7 97 109 124.3 135.6 146.9HANEY UBC 2183.5 143 89.3 111.8 126.7 145.4 159.4 173.3HOPE A 1915.7 39 86.4 120 144 165.6 187.2 208.8KITIMAT 2740.1 17 88.8 109.7 123.6 141.1 154.3 167LADNERBCHPA 981.7 2 43.2 56.2 64.6 75.6 83.5 91.4LANGLEY LOCHIEL 1482.2 101 61.2 75.6 85.2 97.2 106.3 115.2MISSION WEST A. 1841.5 221 72.5 85.4 94.1 105.1 113 121.2NANAIMO DEP.BAY 955.6 8 41.3 50.4 56.4 64.1 69.8 75.4N.VANC.LYNN CR. 2695.7 191 120.2 156.5 180.5 211 233.5 255.8PITT MEADOWS STP 1804.7 5 67.7 85.7 97.4 112.6 123.8 134.9PITT POLDER 2326.2 2 98.9 119 132.2 149 161.5 173.8PORT ALBERNI A 1886 2 87.1 108.5 122.9 140.9 154.3 167.5P. COQUITLAM CITY 1930.9 7 81.1 96.7 107 120 129.6 139.2PORT HARDY 1870.6 22 89.5 116.6 134.6 157.4 174.2 190.8PORT MELLON 3307.1 8 142.6 176.6 199.4 222.8 249.4 270.2PORT MOODY G. 1889.3 130 84.5 105.4 119 136.3 149 161.8PORT RENFREW BCFS 3943.2 3 168 197.3 217 241.4 259.7 277.9PRINCE RUPERT A 2551.6 34 89.5 112.3 127.7 146.6 161 175310APPENDIX BTable B 1. Characteristics of the sixty\u2014one stations used in the analysisof the 24\u2014hour extreme rainfall. (cont.)24-hour Extreme RainfallReturn PeriodMean annual Station (mm)Station Precipitation Elevation 2 5 10 25 50 100(mm) (m)SAANICH DENSMORE 928.4 38 49.4 67 78.5 93.1 103.9 115SANDSPIT A 1359.1 5 52.1 59.8 65 71.3 76.1 80.9SEYMOUR 21A 3291.6 640 124.4 161.5 185.9 209.7 240.2 263.3SEYMOUR 25B 3256.6 762 131.7 170.7 196.9 221.3 253.6 278SEYMOUR ELBOW CR 3427.4 305 114 144.5 164 183.5 208.5 226.8SPRING ISLAND 3155.1 11 121.7 156.7 179.8 209 230.9 252.5STAVE FALLS 2296.8 55 83.8 106.3 121.4 140.4 154.3 168.2STRATHCONA DAM 1381.2 201 61.7 88.1 105.4 127.4 143.8 160.1SURREY KWANTLEN P. 1574.6 93 67.9 89.3 103.4 121.4 134.9 148.1SURREY MUNICIPAL H. 1355.9 76 55.4 68.4 77 87.8 96 103.9TERRACE A 1295.3 217 55.7 79.4 95.3 115.2 129.8 144.5TERRACE PCC 1136.9 58 43.9 58.8 68.6 81.1 90.2 99.4TOFINO A 3295.4 20 128.2 157 176.4 200.6 218.6 236.6VANCOUVER A 1167.4 3 62.2 75.8 85 96.2 104.6 113.3VANCOUVER HARBOUR 1540.3 0 52.8 66.5 75.4 86.6 95 103.4VANCOUVER KITS. 1367.1 23 60.2 76.3 86.9 100.1 110.2 120VANCOUVER PMO 1588.9 59 68.6 94.3 111.6 133 149 164.9VANCOUVER UBC 1288.6 87 57.8 74.2 85.2 98.9 109 119VICT. GONZALES H. 619.2 69 45.1 63.8 76.3 91.9 103.7 115.2VICTORIA INT.A 857.9 19 49.4 63.1 72.2 83.8 92.4 101VICTORIA MARINE R. 1226.9 32 64.8 84.5 97.4 114 126.2 138.5VICTORIA SHELBOURNE 790 38 44.9 61.7 73 87.1 97.4 108VICTORIA UVIC 708.2 46 49.9 67.7 79.7 94.6 105.6 116.6WHITE ROCK STP 1098.2 15 50.4 64.8 74.4 86.6 95.5 104.6311180170160150140130-J120-J110z 100 90 80c\u2019J70 60 50 40 30 20R24=0.040R2=0.886SEE=1O.4mm.aaBU.1I.\u2022\u2022I.I\u20222-YEAR24-HOURRAINFALL____REGRESSIONUNEIIIIIIIItI6001000140018002200260030003400MEANANNUALPRECIPITATION(mm)Fig.Bi.Relationshipofthe2-year24-hourrainfallandmeanannualprecipitationforthesixty-onerecordingstationsincoastalBritishColumbia.3800220210200190180170p160150-j :140z100C\u2019J90 80 70 60 50 40 30MEANANNUALPRECIPITATION(mm)Fig.B2. Relationshipofthe5-year24-hourrainfallandmeanannualprecipitationforthesixty-onerecordingstationsincoastalBritishColumbia....1R24\u20140.050PR2=0.847SEE=15.3mm.. IU\u2022\u2022\u2022I\u20221.1\u2022U\u2022I.\u2022UI\u2022\u2022IU\u20225-YEAR24-HOURRAINFALL____REGRESSIONUNEIIIIII60010001400180022002600300034003800-J-J zFig.B3. Relationshipofthe10-year24-hourrainfallandmeanannualprecipitationfor thesixty-onerecordingstationsincoastalBritishColumbia.260-240-220200180-160140-120-100\u2014 80-60-40 20R24\u20140.057FR2=0.815SEE=19.4mmB\u2022BB\u2022\u2022..IIaB\u2022aB\u2022I ..I\u2022B\u2022B\u2022\u2022\u2022\u202210-YEAR24-HOURRAINFALL____REGRESSIONUNEII\u2022.\u2022IIIIIIIII60010001400180022002600300034003800MEANANNUALPRECIPITATION(mm)-J-J zR24=0.066PR2=0.776SEE=24.6mm.320300280260240220200180160140120100 80 60 40....\u2022:I.\u2022\u2022\u2022\u2022.II25-YEAR24-HOURRstJNFA1i____REGRESSIONUNE60010001400180022002600300034003800MEANANNUALPRECIPITATION(mm)Fig.B4.Relationshipofthe25-year24-hourrainfallandmeanannualprecipitationforthesixty-onerecordingstationsincoastalBritishColumbia.R24=0.073P-R2=0.762SEE=28.6mmI360340320300280__260E 240 220LI.. z200140 120100 80 60 40C\u2019B\u2022B.I..I..B\u2022II\u2022\u2022I.I III.I.\u2022600I50-YEAR24-HOURRAINFALL____REGRESSIONUNEIIIIIIIIIIIII10001400180022002600300034003800MEANANNUALPRECIPITATION(mm)Fig.B.5Relationshipofthe50-year24-hourrainfallandmeanannualprecipitationfor thesixty-onerecordingstationsincoastalBritishColumbia.-J-J z 2R24\u20140.079PR2=0.741SEE=32.8mm400350300250200-150-100 50 0..I..II\u20221II.\u201d.I.II:..I\u2022100-YEAR24-HOURRPJNFALL____REGRESSIONUNEIIIIIIIIIIIIIII60010001400180022002600300034003800MEANANNUALPRECIPITATION(mm)Fig.86.Relationshipofthe100-year24-hourrainfallandmeanannualprecipitationforthesixty-onerecordingstationsincoastalBritishColumbia.APPENDIX CCHARACTERISTICS OF THE 44 BASINS USED FOR THE TESTING OFTHE MODIFIED SNYDER FORMULA3APPENDIX CTable Cl. Characteristics of the basins used in theindependent test of the modified Snyder formula*.Basin Time Lag Area Length Str.Slope(h) (km2) (km) (mim)1 0.083 0.08 0.372 0.04892 0.167 0.21 0.656 0.03993 0.117 0.05 0.432 0.05444 0.283 0.44 1.2 0.02745 0.025 0.005 0.109 0.09786 0.05 0.01 0.205 0.08537 0.315 0.1 0.445 0.01518 0.443 1.17 1.76 0.01299 0.476 0.34 0.646 0.006610 0.436 0.18 0.838 0.005411 1.87 3.91 3.03 0.005112 1.81 4.01 2.7 0.005813 0.625 0.36 0.579 0.00614 1.9 18.5 7.96 0.005315 0.24 0.31 0.969 0.006516 0.31 1.22 1.36 0.03717 0.286 0.75 1.52 0.011518 0.117 0.03 0.206 0.062519 0.419 1.35 1.76 0.020320 0.116 0.1 0.305 0.047521 0.267 0.7 0.997 0.021722 0.417 1.4 2 0.007623 0.139 0.012 0.173 0.014124 0.165 0.011 0.178 0.014825 1.42 7.8 6.58 0.00626 0.636 1.94 2.74 0.00527 8.3 109 18.5 0.0030228 6.4 58.8 8.21 0.0057429 9 324 24.2 0.0021730 9.3 89.1 19.5 0.0048531 7.4 259 21.6 0.0067132 9.5 165 30.3 0.0043533 10.8 59.6 11.7 0.0026834 6.9 85.5 16.4 0.0067735 19.6 124 27.5 0.0015336 13.5 132 19.8 0.0012137 24 1210 97.4 0.0034319APPENDIX CTable Cl. Characteristics of the basins used in theindependent test of the modified Snyder formula*. (cont.)Basin Time Lag Area Length Str.Slope(h) (km2) (krn) (rn\/rn)38 22 1650 77.2 0.004639 22 824 24.1 0.002540 13 1130 85.3 0.004141 40 5850 196 0.001442 21.5 839 61.2 0.004543 13 642 48.3 0.008344 10 210 34.9 0.0058* Data from Watt and Chow, 1985320","attrs":{"lang":"en","ns":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","classmap":"oc:AnnotationContainer"},"iri":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","explain":"Simple Knowledge Organisation System; Notes are used to provide information relating to SKOS concepts. 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